Data classifier system, data classifier method and data classifier program

ABSTRACT

A data classifier system of the present invention selects a plurality of classifications correlated to data groups so as to output classification axes based on hierarchical classifications and data groups. The data classifier system includes a basic category accumulation means, a classification axis candidate reduction means and a priority calculation means. The basic category accumulation means accumulates classifications serving as basic categories used for desired classifications in advance. The classification axis candidate reduction means selects a plurality of classifications from among classifications descendant from each basic category so as to create classification axis candidates, thus reducing classification axis candidates subjected to calculations based on data quantity of classifications and hierarchical distances of classifications. The priority calculation means calculates priority in displaying classification axis candidates, the number of which is reduced by the classification axis candidate reduction means.

TECHNICAL FIELD

The present invention relates to data classifier systems, dataclassifier methods and data classifier programs.

The present application claims priority on Japanese Patent ApplicationNo. 2008-195895 filed in Japan on Jul. 30, 2008, the content of which isincorporated herein by reference.

BACKGROUND ART

Data reduction techniques usually adopt hierarchical classifications toreduce numerous data. In the database disclosed in Non-Patent Document1, for example, hierarchical classifications referred to as MeSH termsare assigned to documents. In the patent database operated by the PatentOffice, for example, a plurality of hierarchical classifications such asF terms is assigned to patent documents. Hereinafter, hierarchicalclassifications assigned to data will be referred to as classificationhierarchy.

Non-Patent Document 2 discloses a relevant art that allows users to readdocuments assigned with the aforementioned hierarchical classifications.Non-Patent Document 2 discloses a system, referred to as OLAP, whichextends multifaceted data display methods so as to achieve high-speedprocessing with respect to a very large hierarchy. In the relevant artdisclosed in Non-Patent Document 2, users are each allowed to select aclassification so as to display data quantity included in theclassification at a high speed. In the relevant art disclosed inNon-Patent Document 2, users are each allowed to select a classificationon a vertical axis and a classification on a horizontal axis so as todisplay the result by way of a table form.

Owing to the aforementioned operation, the relevant art disclosed inNon-Patent Document 2 is able to display a list of documents assignedwith a plurality of classifications. Hereinafter, a group ofclassifications used for displaying data will be referred to as aclassification axis.

In the case of a very large classification hierarchy, however, it isdifficult for users to determine which classification needs to beselected. As documents used in the system disclosed in Non-PatentDocument 2, for example, there are about five-hundred thousand documentsand about thirty-four hundred thousand classifications. Therefore, it isvery difficult for users to select display-wished classifications withinthe classification hierarchy.

Non-Patent Document 3 discloses a relevant classification selectingmethod. Non-Patent Document 3 discloses retrieval-resultant documentlists via document retrieval along with a method of displayingclassification axes related to retrieval-resultant document lists.According to the method disclosed in Non-Patent Document 3, keywords areinput to retrieve documents so as to display retrieval-resultantdocuments while a plurality of classifications pre-assigned toretrieval-resultant documents is displayed as a classification axis.When displaying the classification axis, each classification needs to beselected because the display area is limited.

In addition, Non-Patent Document 3 discloses a method of selecting anupper-limit place among classifications in an order counting from aclassification assigned to a larger number of retrieval-resultantdocuments and a method of selecting a combination of classificationswhich is able to display the largest number of retrieval-resultantdocuments. Furthermore, Non-Patent Document 3 discloses a method ofminimizing cost such as the number of times a mouse needs to be clickedto display all the content.

PRIOR ART DOCUMENTS Non-Patent Documents

-   Non-Patent Document 1: “PubMed”, National Center for Biotechnology    Information, [Retrieved on Jul. 4, 2008], Internet <URL:    http://www.ncbi.nlm.nih.gov/sites/entrez!db=PubMed>-   Non-Patent Document 2: Inoguchi, Takeda, “An OLAP Systems for Text    Analytics”, DEICE Technical Report, vol. 48, No. SIG11 (T0D34), p.    58-p. 68-   Non-Patent Document 3: Wisam Dakka, Panagiotis G. Iperirotis,    Kenneth R. Wood, “Automatic Construction of Multifaceted Browsing    Interfaces”, Proc. Of CIKM' 05, p. 768-p. 775

DISCLOSURE OF INVENTION Problem to be Solved by the Invention

In the relevant arts disclosed in Non-Patent Documents 1 through 3,however, numerous calculations are needed to calculate priorities ofclassification axes, thus allowing users to select recognizableclassification axes. The reason is that the relevant arts disclosed inNon-Patent Documents 1 through 3 are unable to effectively reduce thenumber of classification axes subjected to calculations.

Since the method of selecting classifications, disclosed in Non-PatentDocument 3, can reduce a calculation time, it is possible to selectclassifications in an order counting from a classification assigned to alarger amount of data. When selecting a second place and onwards ofclassifications, it is possible to select classifications each assignedto a large amount of data from among classifications which are notcorrelated to already selected classifications.

However, the method disclosed in Non-Patent Document 3 simply selectsclassifications each assigned to a large amount of data but cannotselect classifications according to the semantic relevancy ofclassifications. For this reason, the method disclosed in Non-PatentDocument 3 is unable to effectively reduce the number ofclassifications. Therefore, it is preferable to select classificationsaccording to the semantic relevancy of classifications.

Thus, the problem to be solved by the invention is to provide a dataclassifier system, a data classifier method and a data classifierprogram, each of which is able to reduce a time of calculating priorityon classification axes upon being provided with classification hierarchyand data groups suited to classifications.

Means to Solve the Problem

The present invention is created to solve the above problems, whereinthe present invention is directed to a data classifier system whichselects a plurality of classifications correlated to data groups basedon hierarchical classifications and data groups so as to outputclassification axes. The data classifier system is constituted of abasic category accumulation means which accumulates classificationsserving as basic categories used for selecting desired classificationsin advance, a classification axis candidate reduction means whichselects a plurality of classifications from among classificationsdescendant from each basic category so as to create classification axiscandidates and which reduces the number of classification axiscandidates subjected to calculations based on data quantity ofclassifications and hierarchical distances of classifications, and apriority calculation means which calculates priority in displayingclassification axis candidates reduced by the classification axiscandidate reduction means.

Another aspect of the present invention is directed to a data classifiersystem which selects a plurality of classifications correlated to datagroups so as to create classification axes based on hierarchicalclassifications and data groups and which outputs combinations ofclassification axes as multidimensional classification axes. The dataclassifier system is constituted of a basic category accumulation meanswhich accumulates classifications serving as basic categories used forselecting desired classifications in advance, a classification axiscandidate reduction means which creates classification axes based oncombinations of classifications, each correlated to at least one data,among classifications descendant from each basic category and whichreduces the number of classification axis candidates subjected tocalculations based on data quantity of classifications and hierarchicaldistances of classifications, a multidimensional classification axiscandidate creation means which creates multidimensional classificationaxis candidates combining classification axis candidates reduced by theclassification axis candidate reduction means, and a multidimensionalpriority calculation means which calculates priority on multidimensionalclassification axis candidates based on hierarchical distances ofclassifications in a classified hierarchy.

The present invention is directed to a data classifier method whichselects a plurality of classifications correlated to data groups so asto output classification axes based on hierarchical classifications anddata groups. The data classifier method includes a classification axiscandidate reduction process which accumulates classifications serving asbasic categories used for selecting desired classifications in adatabase in advance, which selects a plurality of classifications fromamong classifications descendant from each basic category so as tocreate classification axis candidates, and which reduces the number ofclassification axis candidates subjected to calculations based on dataquantity of classifications and hierarchical distances ofclassifications, and a priority calculation process which calculatespriority in displaying the reduced number of classification axiscandidates.

Another aspect of the present invention is directed to a data classifiermethod which selects a plurality of classifications correlated to datagroups based on hierarchical classifications and data groups so as tocreate classification axes and which outputs multidimensionalclassification axes combining a plurality of classification axes. Thedata classifier method includes a classification axis candidatereduction process which accumulates classifications serving as basiccategories used for selecting desired classifications in a database inadvance, which creates classification axes based on combinations ofclassifications correlated to at least one data among classificationsdescendant from each basic category, and which reduces the number ofclassification axis candidates subjected to calculations based on dataquantity of classifications and hierarchical distances ofclassifications, a multidimensional classification axis candidatecreation process which creates multidimensional classification axiscandidates each combining the reduced number of classification axiscandidates, and a multidimensional priority calculation process whichcalculates priority on multidimensional classification axis candidatesbased on hierarchical distances of classifications in a classifiedhierarchy.

The present invention is directed to a data classifier program a dataclassifier program which selects a plurality of classificationscorrelated to data groups so as to output classification axes based onhierarchical distances and data groups. The data classifier programcauses a computer having a basic category accumulation means, whichaccumulates classifications serving as basic categories used forselecting desired classifications, to execute a classification axiscandidate reduction process which selects a plurality of classificationsfrom among classifications descendant from each basic category so as tocreate classification axis candidates and which reduces the number ofclassification axis candidates subjected to calculations based on dataquantity of classifications and hierarchical distances ofclassifications, and a priority calculation process which calculatespriority in displaying the reduced number of classification axiscandidates.

Another aspect of the present invention is directed to a data classifierprogram which selects a plurality of classifications correlated to datagroups based on hierarchical classifications and data groups so as tocreate classification axes and which outputs multidimensionalclassification axes combining a plurality of classification axes. Thedata classifier program causes a computer having a basic categoryaccumulation means, which accumulates classifications serving as basiccategories used for selecting desired classifications in advance, toexecute a classification axis candidate reduction process which createsclassification axes based on combinations of classifications correlatedto at least one data among classifications descendant from each basiccategory, and which reduces the number of classification axis candidatessubjected to calculations based on data quantity of classifications andhierarchical distances of classifications, a multidimensionalclassification axis candidate creation process which createsmultidimensional classification axis candidates each combining thereduced number of classification axis candidates, and a multidimensionalpriority calculation process which calculates priority onmultidimensional classification axis candidates based on hierarchicaldistances of classifications in a classified hierarchy.

Effect of the Invention

The present invention is able to reduce a calculation time on prioritiesof classification axes upon being provided with classification hierarchyand data groups suited to classifications.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 A block diagram showing an example of the constitution of a dataclassifier system according to the present invention.

FIG. 2 An illustration showing an example of information stored in aclassified hierarchy accumulation unit.

FIG. 3 An illustration showing an example of information stored in abasic category accumulation unit.

FIG. 4 An illustration showing an example of information stored in adata accumulation unit.

FIG. 5 A flowchart showing an example of a data classification processperformed by a data classifier system.

FIG. 6(A) shows an example of a table including records correlated witha classification axis ID, a basic category, a classification and ascore. FIG. 6(B) shows an example of a table including recordscorrelated with a classification axis ID, a classification and a dataID.

FIG. 7 A block diagram showing an example of the constitution of a dataclassifier system according to a second embodiment.

FIG. 8 An illustration showing an example of a classified hierarchy.

FIG. 9 A flowchart showing an example of a data classification processperformed by the data classifier system of the second embodiment.

FIG. 10(A) shows an example of a classification-specified data quantitytable. FIG. 10(B) shows an example of a data-specified classificationnumber table.

FIG. 11 A block diagram showing an example of the constitution of a dataclassifier system according to a third embodiment.

FIG. 12 An illustration showing an example of information which adisplay means displays in list form.

FIG. 13 An illustration showing an example of information which thedisplay means displays in table form.

FIG. 14 A block diagram showing an example of the constitution of a dataclassifier system according to a fourth embodiment.

FIG. 15 A flowchart showing an example of a data classification processperformed by the data classifier system of the fourth embodiment.

FIG. 16(A) shows an example of a table including records correlated witha dimensional ID, a classification axis ID and a score. FIG. 16(B) showsan example of a table including records correlated with a classificationaxis ID, a basic category and a classification. FIG. 16(C) shows anexample of a table including records correlated with a classificationaxis ID, a classification and a data ID.

FIG. 17 A block diagram showing an example of the constitution of a dataclassifier system according to a fifth embodiment.

FIG. 18(A) shows an example of a classification-specified data quantitytable. FIG. 18(B) shows an example of a data-specified classificationnumber table.

FIG. 19 A block diagram showing an example of the constitution of a dataclassifier system according to a sixth embodiment.

FIG. 20 An illustration showing an example of information which amultidimensional display means displays in list form.

FIG. 21 An illustration showing an example of information which themultidimensional display means displays in table form.

FIG. 22 A block diagram showing an example of the constitution of a dataclassifier system according to a seventh embodiment.

FIG. 23 A block diagram showing an example of the constitution of a dataclassifier system according to an eighth embodiment.

FIG. 24 A block diagram showing an example of a minimum constitution ofa data classifier system.

MODE FOR CARRYING OUT THE INVENTION

Hereinafter, the present invention will be described with reference tospecific embodiments. A skilled person in the art may employ a varietyof different embodiments based on the description of the presentinvention; hence, the present invention is not necessarily limited toembodiments which are illustrated herein for the purpose of explanation.

Hereinafter, various embodiments of the present invention will bedescribed. First, the outline of a data classifier system according tothe present invention will be described. When users observeclassification axes in data classifications, it is deemed necessary tomaintain the semantic independence between data quantity andclassifications. In addition, it is preferable that data quantity be aslarge as possible. When comprehending the outline by use ofclassification axes, for example, it is impossible to comprehend datagroups unless a large amount of data is assigned to each classificationaxis.

When comprehending and reducing the outline by use of classificationaxes, for example, it is not possible to determine whether or not otherrelevant classifications are involved on the basis of similarclassifications alone with regard to the semantic independence. For thisreason, users may need to repeat selecting operations. Since similarclassification may fall within the brother relationship in theclassified hierarchy, the present invention employs distances ofclassifications in the classified hierarchy in order to evaluate thesemantic independence.

The data classifier system of the present invention is constituted of aclassification axis candidate reduction means and an index calculationmeans. Upon being provided with the classified hierarchy and data groupscorrelated to classifications, the classification axis candidatereduction means creates classification axis candidates such thathierarchical distances of classifications and data quantity satisfyspecific conditions. The index calculation means determines prioritieson classification axis candidates based on hierarchical distances ofclassifications in the classified hierarchy considering theindependence.

The data classifier system of the present invention is constituted of aclassification axis candidate reduction means and a secondary indexcalculation means. Upon being provided with the classified hierarchy anddata groups correlated to classifications, the classification axiscandidate reduction means creates classification axis candidates suchthat hierarchical distances of classifications and data quantity satisfyspecific conditions. In addition, the secondary index calculation meansdetermines priorities on classification axis candidates based onhierarchical distances of classifications in the classified hierarchyaccording to the dependency, based on depths of classifications in theclassified hierarchy according to the concreteness, based on dataquantity allocated to classifications according to the exhaustivity, andbased on data redundancies according to the uniqueness.

The data classifier system of the present invention is constituted of aclassification axis candidate reduction means, a secondary indexcalculation means and a display means. Upon being provided with theclassified hierarchy and data groups correlated to classifications, theclassification axis candidate reduction means creates classificationaxis candidates such that hierarchical distances of classifications anddata quantity satisfy specific conditions. The secondary indexcalculation means determines priorities on classification axiscandidates based on hierarchical distances of classification and dataquantity considering the independence, based on depths ofclassifications in the classified hierarchy considering theconcreteness, based on data quantity allocated to classificationsconsidering the exhaustivity, and based on data redundancies consideringthe uniqueness. The display means uses classification axes having higherpriorities among classification axis candidates so as to display a datalist and classifications assigned to each classification axis.

The data classifier system of the present invention is constituted of amultidimensional classification axis candidate reduction means and amultidimensional index calculation means. Upon being provided with theclassified hierarchy and data groups allotted to classifications, themultidimensional classification axis candidate reduction means createsclassification axes based on classifications satisfying conditions forlimiting hierarchical distances of classifications and data quantity,thus creating multidimensional classification axes based on combinationsof classification axes. The multidimensional index calculation meanscalculates priorities on multidimensional classification axis candidatesby use of hierarchical distances of classifications assigned toclassification axes of respective dimensions.

The data classifier system of the present invention is constituted of amultidimensional classification axis candidate reduction means and asecondary multidimensional index calculation means. Upon being providedwith the classified hierarchy and data groups allocated toclassifications, the multidimensional classification axis candidatereduction means creates classification axes based on classificationssatisfying conditions for limiting hierarchical distances ofclassifications and data quantity, thus creating multidimensionalclassification axes based on combinations of classification axes. Thesecondary multidimensional index calculation means determines prioritieson multidimensional classification axis candidates by use of at leastone of depths of classification axis candidates of respective dimensionsin the classified hierarchy, data quantity and data redundancies inaddition to hierarchical distances of classifications assigned toclassifications of respective dimensions.

The data classifier system of the present invention is constituted of amultidimensional classification axis candidate reduction means, asecondary multidimensional index calculation means and amultidimensional display means. Upon being provided with the classifiedhierarchy and data groups allocated to classifications, themultidimensional classification axis candidate reduction means createsclassification axes based on classifications satisfying conditions forlimiting hierarchical distances of classifications and data quantity,thus creating multidimensional classification axes based on combinationsof classification axes. The secondary multidimensional index calculationmeans determines priorities on multidimensional classification axiscandidates by use of at least one of depths of classification axiscandidates of respective dimensions in the classified hierarchy, dataquantity and data redundancies in addition to hierarchical distances ofclassifications assigned to classification axes of respectivedimensions. The multidimensional display means displays classificationaxes of respective dimensions and classifications, thus displaying alist of data upon reducing data groups by selecting one or pluralclassifications.

The data classifier system of the present invention is constituted of adata retrieval means, a multidimensional classification axis candidatereduction means, a secondary multidimensional index calculation meansand a multidimensional display means. Upon being provided with theclassified hierarchy and data groups assigned to classifications, thedata retrieval means retrieves data so as to reduce retrieval-resultantdata groups. The multidimensional classification axis candidatereduction means creates classification axes based on classificationssatisfying conditions for limiting hierarchical distances ofclassifications and retrieval-resultant data quantity, thus creatingmultidimensional classification axes based on combinations ofclassification axes. The secondary multidimensional index calculationmeans determines priorities on multidimensional classification axiscandidates by use of at least one of depths of classification axiscandidates of respective dimensions in the classified hierarchy,retrieval-resultant data quantity and retrieval-resultant dataredundancies in addition to hierarchical distances of classificationsassigned to classification axes of respective dimensions. Themultidimensional display means displays classifications andclassification axes of respective dimensions, thus displaying a list ofretrieval-resultant data upon reducing data groups by selecting one orplural classifications.

The data classifier system of the present invention is constituted of adata correlation means, a data retrieval means, a multidimensionalclassification axis candidate reduction means, a secondarymultidimensional index calculation means and a multidimensional displaymeans. Upon being provided with the classified hierarchy and data groupsallocated to classifications, the data correlation means performs acorrelation procedure on data not correlated with classifications ordata insufficiently correlated with classifications. The data retrievalmeans retrieves data so as to reduce retrieval-resultant data groups.The multidimensional classification axis candidate reduction meanscreates classification axes based on classifications satisfyingconditions for limiting hierarchical distances of classifications andretrieval-resultant data quantity, thus creating multidimensionalclassification axes based on combinations of classification axes. Thesecondary multidimensional index calculation means determines prioritieson multidimensional classification axis candidates by use of at leastone of depths of classification axis candidates of respective dimensionsin the classified hierarchy, retrieval-resultant data quantity andretrieval-resultant data redundancies in addition to hierarchicaldistances of classifications assigned to classification axes ofrespective dimensions. The multidimensional display means displaysclassifications and classification axes of respective dimensions, thusdisplaying a list of retrieval-resultant data upon reducing data groupsby selecting one or plural classifications.

First Embodiment

Next, a first embodiment of the present invention will be described withreference to the drawings. FIG. 1 is a block diagram showing an exampleof the constitution of a data classifier system according to the presentinvention. As shown in FIG. 1, the data classifier system includes aninput means 101, a classification axis candidate reduction means 1021,an index calculation means 103, an output means 104, a classifiedhierarchy accumulation unit 201, a basic category accumulation unit 202and a data accumulation unit 203.

Specifically, in the present embodiment, the data classifier system isconfigured of an information processing device such as a personalcomputer which operates according to programs. In this connection, thedata classifier system is not necessarily limited to a singleinformation processing device but can be embodied using a plurality ofinformation processing devices, for example.

Specifically, the classified hierarchy accumulation unit 201 isconfigured of a database device such as a magnetic disk device and anoptical disk device. The classified hierarchy accumulation unit 201accumulates the information representing the classified hierarchy andthe hierarchical relationship in advance. FIG. 2 shows an example of theinformation stored in the classified hierarchy accumulation unit 201. Asshown in FIG. 2, the classified hierarchy accumulation unit 201integrates records in the parent-child relationship and stores a tabledescribing the correlation between parent classifications and childclassifications.

The table shown in FIG. 2 is created by a system operator in advance forthe purpose of data reduction, for example, and accumulated in theclassified hierarchy accumulation unit 201. The data classifier systemcan automatically retrieve classifications based on data accumulated ina document database so as to create a table, for example, which can bestored in the classified hierarchy accumulation unit 201.

In an example of FIG. 2, parent classifications representclassifications serving as parents in the parent-child relationship.Child classifications represent classifications serving as children byuse of delimiters of “,”. In this illustration, “ . . . ” denotesomission marks.

FIG. 2 shows an example of a storage method; for instance, theclassified hierarchy accumulation unit 201 can divide and store childclassifications by records or store hierarchical structure data. For thesake of simplifying expressions, the present embodiment employs theexpression shown in FIG. 2.

Specifically, the basic category accumulation unit 202 is configured ofa database device such as a magnetic disk device and an optical diskdevice. The basic category accumulation unit 202 accumulates names ofclassifications serving as the basis of classification axes in advance.FIG. 3 shows an example of the information stored in the basic categoryaccumulation unit 202. As shown in FIG. 3, the basic categoryaccumulation unit 202 stores a list of basic categories.

In this connection, basic categories stored in the basic categoryaccumulation unit 202 are selected from among classifications stored inthe classified hierarchy accumulation unit 201 in advance. In this case,for example, basic categories can be selected by a system operator inadvance and accumulates in the basic category accumulation unit 202;alternatively, the data classifier system can automatically extractbasic categories from the classified hierarchy accumulation unit 201,thus storing them in the basic category accumulation unit 202.

Specifically, the data accumulation unit 203 is configured of a databasedevice such as a magnetic disk device and an optical disk device. Thedata accumulation unit 203 accumulates the correlation betweenclassifications and data in advance. Alternatively, the dataaccumulation unit 203 can accumulate the attribute information such ascreation dates/times and other attributes of data and the contents. FIG.4 shows an example of the information stored in the data accumulationunit 203. As shown in FIG. 4, the data accumulation unit 203 is adatabase storing records embracing the correlation between a data ID,content and a counterpart classification.

In an example of FIG. 4, “ . . . ” denotes omission marks. In thisexample of FIG. 4, the data ID is an identifier for identifying data. Inaddition, the counterpart classification indicates a classificationcorrelated to data identified by the data ID by use of a delimiter “,”.In this connection, FIG. 4 shows an example of a storage method; hence,the data accumulation unit 203 can store records embracing the attributeinformation representing attributes such as creation dates other thandetailed content.

The aforementioned data are collected by a system operator in advance,for example, and accumulated in the data accumulation unit 203. Inaddition, the data classifier system can collect accumulated data ofdatabases via networks, for example, so as to store them in the dataaccumulation unit 203.

Specifically, the input means 101 is configured of a CPU, an inputdevice such as a keyboard and a mouse, and an input/output interface inan information processing device which operates according to programs.The input means 101 implements a function of inputting various pieces ofinformation according to user's operations. Alternatively, the inputmeans 101 implements a function of receiving (inputting) the inputinformation from the other system. In the data classifier system of thepresent embodiment, the input means 101 may receive (input) the numberof classifications N according to user's operations.

Specifically, the classification axis candidate reduction means 1021 isconfigured of a CPU of an information processing device which operatesaccording to programs. Based on classifications stored in the classifiedhierarchy accumulation unit 201 and basic categories stored in the basiccategory accumulation unit 202, the classification axis candidatereduction means 1021 implements a function of combining theaforementioned number of classifications and thereby creatingclassification axis candidates.

In the present embodiment, the classification axis Candidate reductionmeans 1021 does not create classification axis candidates by combiningall descendant classifications belonging to basic categories but reducesthe number of classification axes created based on data quantity ofclassifications and hierarchical distances of classifications. Thus, theclassification axis candidate reduction means 1021 is able to reduce thenumber of classification axes subjected to priority calculations, thusachieving high-speed calculations.

In this connection, the term “data quantity of classifications”represents the amount of data correlated to classifications. Inaddition, “data of classifications” indicate data directly correlated toclassifications or data correlated to classifications and descendantclassifications. In the present embodiment, “data of classifications”are defined as data correlated to classifications and descendantclassifications. Large data quantity may assume high exhaustivity. Sinceclassifications defining classifications precisely represent datagroups, they are deemed useful in comprehending configurations of data.

The term “hierarchical distances of classifications” represents theshortest/longest path lengths leading to common ancestors or theshortest/longest path lengths leading to common descendants. In thiscase, classifications having longer hierarchical distances are highlyindependent classifications in terms of semantics.

When selecting classifications based on the above criteria, theclassification axis candidate reduction means 1021 selectsclassification axes embracing classifications, satisfying conditions forlimiting classifications to ones having larger data quantity than thepredetermined value and for limiting classifications to ones whose dataquantity is ranked in a higher place of ordering than other ones havingsmall data quantity and conditions for limiting classifications to oneswhose hierarchical distances are longer than the predetermined value orwhose hierarchical distances fall within a certain range, as well asdescendant classifications derived from limited classifications.

Specifically, the index calculation means 103 is configured of a CPU ofan information processing device which operates according to programs.The index calculation means 103 implements a function of receiving(inputting) classification axis candidates from the classification axiscandidate reduction means 1021, referring to the information stored inthe classified hierarchy accumulation unit 201 and the informationstored in the data accumulation unit 203, and calculating priorities onclassification axis candidates.

The index calculation means 103 calculate priorities based onhierarchical distances of classifications in the classified hierarchy.Herein, the term “hierarchical distances of classifications” representsthe shortest/longest path lengths leading to common ancestors or theshortest/longest path lengths leading to common descendants. Inaddition, the index calculation means 103 calculates priorities asaverage values and maximum/minimum values among hierarchical distancesof classifications on classification axes.

In the present embodiment, the index calculation means 103 calculatesthe shortest path lengths leading to common ancestors as hierarchicaldistances of classifications while calculating priorities as averagevalues among hierarchical distances. This indicates that classificationaxes having longer hierarchical distances are highly independent interms of the semantics.

Specifically, the output means 104 is configured of a CPU, a displaydevice such as a display, and an input/output interface of aninformation processing device which operates according to programs. Theoutput means 104 implements a function of receiving (inputting) pairs ofclassification axis candidates and priorities from the index calculationmeans 103. In addition, the output means 104 implements a function ofoutputting input pairs of classification axis candidates and prioritiestogether with data belonging to classifications. In this connection, itis possible to determine the number of classification axes output fromthe output means 104 in advance. As an output method, the output means104 may display data on a display device such as a display or outputfiles into storage media (e.g. CD-ROM) or other programs.

In the present embodiment, a storage device of an information processingdevice (not shown) implementing the data classifier system storesvarious programs to classify data. For instance, the storage device ofthe information processing device implementing the data classifiersystem stores data classifier programs causing a computer to execute aclassification axis candidate reduction process, in which classificationaxis candidates are created upon selecting a plurality ofclassifications from among classifications descendant from basiccategories and subsequently reduced to ones subjected to calculationsbased on data quantity of classifications and hierarchical distances ofclassifications, and a priority calculation process for calculatingpriorities of displaying reduced classification axis candidates.

Next, the operation will be described. FIG. 5 is a flowchart showing anexample of a data classifier process performed by the data classifiersystem.

First, the input means 101 of the data classifier system receives thenumber of classifications N according to a user's operation (step S1).For instance, the input means 101 inputs N=3 as the number ofclassifications. Next, the classification axis candidate reduction means1021 obtains (extracts) basic categories of classification axes from thebasic category accumulation unit 202 (step S2). In an example of FIG. 3,for example, the classification axis candidate reduction means 1021extracts the information such as “society”, “nature” and “culture” fromthe basic category accumulation unit 202.

Next, the classification axis candidate reduction means 1021 reduces thenumber of classification axes which are created based on data quantityof classifications and hierarchical distances of classifications (stepS31).

Next, the classification axis candidate reduction means 1021 refers tothe information stored in the classified hierarchy accumulation unit 201so as to combine classifications based on hierarchical distances ofclassifications, thus creating classification axes (step S32). In thepresent embodiment, the classification axis candidate reduction means1021 employs lengths between classifications leading to common ancestorsas hierarchical distances of classifications. Based on classificationsreduced in step S31 and their descendant classifications, theclassification axis candidate reduction means 1021 createsclassification axes embracing at least one pair of classifications whosehierarchical distances are longer than the predetermined value. In thisconnection, classification axes do not embrace classifications linkedtogether via the ancestor-descendant relationship.

For instance, the classification axis candidate reduction means 1021creates classification axes using classifications whose hierarchicaldistances are 3 or more among previously reduced classificationsrepresenting “living”, “medical care”, “family”, “home”, “medicine”,“transplant” and “health care”. In the present embodiment, for example,a classification of “medicine” has a hierarchical distance of 3 or morecounted from a classification of “living”. Therefore, the classificationaxis candidate reduction means 1021 creates a classification axis usingthe classifications of “living” and “medicine” and any one of otherpreviously reduced classifications. In this case, the classificationaxis candidate reduction means 1021 selects the two classifications of“living” and “medicine” as well as other target classifications whichare not classifications linked in the ancestor-descendant relationship.Therefore, the present embodiment, the classification axis candidatereduction means 1021 creates a classification axis (society: living,medicine, transplant).

Similarly, classifications of “home”, “family” and “living” havehierarchical distances of 3 or more counted from a classification of“medical care”. Therefore, the classification axis candidate reductionmeans 1021 creates a classification axis (society: medical care, home,family), a classification axis (society: medical care, home, healthcare) and a classification axis (society: medical care, family, healthcare).

Although the classification axis candidate reduction means 1021 createsclassifications such that any one of classifications on eachclassification axis satisfies a hierarchical distance condition, it ispossible to create classifications all of which satisfies hierarchicaldistance conditions.

As described above, upon executing the process of step S32,classifications having no semantic independences are precluded. In theabove example, the classification axis candidate reduction means 1021creates classification axes to preclude a classification axis (society:family, home, health care).

Next, the index calculation means 103 obtains (inputs) classificationaxis candidates from the classification axis candidate reduction means1021 so as to calculate priorities on classification axes with referenceto the information stored in the classified hierarchy accumulation unit201 (step S4). In the present embodiment, the index calculation means103 secures semantic independences among classifications whencalculating priorities; hence, it calculates average values amonghierarchical distances of classifications. In the present embodiment,the index calculation means 103 calculates shortest paths leading toclassifications of common ancestors as hierarchical distances ofclassifications.

Specifically, the index calculation means 103 calculates prioritiesaccording to Equation (1).

Priority(X:C)=1/Max(X)×1/(2×number of combinations)×ΣComDist(ci,cj)  (1)

In Equation (1), X denotes a basic category, and C denotesclassifications. In addition, ci, cj denotes a classification i and aclassification j respectively. Max(X) denotes the depth of a deepestclassification among descendant classifications belonging to the basiccategory X. ComDist(ci,cj) denotes a distance between classifications ciand cj. Furthermore, the number of combinations is the number of timesfor selecting two classifications out of classifications C. The reasonwhy Equation (1) divides the average value by Max(X) is that basiccategories have different depths.

Using Equation (1), the index calculation means 103 calculates apriority on a classification axis (society: home, health care, medicalcare) according to Equation (2).

Priority(society: home, health care, medical care)=½×⅙×(ComDist(home,health care)+ComDist(home, medical care)+ComDist(health care, medicalcare))  (2)

Since the classifications of “family” and “health care” has the commonancestor of “living”, the index calculation means 103 producesComDist(home, health care)=2. Since the classifications of “home” and“medical care” have the common ancestor of “society”, the indexcalculation means 103 produces ComDist(home, medical care)=3 andComDist(health care, medical care)=3. Therefore, the index calculationmeans 103 calculates a priority on a classification axis (society: home,health care, medical care) according to Equation (3).

Priority(society: home, health care, medical care)=½×⅙×(ComDist(home,health care)+ComDist(home, medical care)+ComDist(health care, medicalcare))=½×⅙×(2+3+3)=0.67  (3)

Next, the output means 104 output classification axes and datacorrelated to priorities based on the calculation result of the indexcalculation means 103 (step S5). FIG. 6(A) and FIG. 6(B) show examplesof the information output from the output means 104. In these examplesshown in FIG. 6(A) and FIG. 6(B), the output means 104 outputs twotables.

As shown in FIG. 6(A), for example, the output means 104 outputs a tableincluding records correlated with a classification axis ID, a basiccategory, classifications and a score. In FIG. 6(A), a record describedin one row indicates a single classification axis. The classificationaxis ID is an ID for identifying classification axis candidates. Theclassifications include a plurality of classifications divided with adelimiter “,”.

As shown in FIG. 6(B), for example, the output means 104 outputs a tableincluding records correlated with a classification axis ID, aclassification and a data ID. In FIG. 6(B), each record is correlated toeach classification on each classification axis. The data ID includes aplurality of data IDs divided with a delimiter “,”. In thisillustration, “ . . . ” denotes omission marks.

In this connection, the output methods of FIG. 6(A) and FIG. 6(B) areillustrative; for instance, the output means 104 can output a singletable combining two tables or additionally outputs a new table includingthe attribute data of respective data.

According to the aforementioned constitution, it is possible to selectclassification axes by use of semantic independences of classifications.This makes it possible for users to select comprehensive classificationaxes.

As described above, the present embodiment selects a plurality ofclassifications from among descendant classifications of basiccategories, thus creating classification axis candidates. In addition,it reduces the number of classification axis candidates subjected tocalculations based on data quantity of classifications and hierarchicaldistances of classifications. Subsequently, it calculates priorities ofdisplaying reduced candidate priority axes. Since priority calculationsare performed on reduced classification axis candidates alone, it ispossible to reduce calculation times of priorities on classificationaxes. Therefore, upon being provided with the classified hierarchy anddata groups assigned to classifications, it is possible to reducecalculation times of priorities on classification axes.

Since the present embodiment is able to effectively reduce the number ofclassifications by use of hierarchical distances of classifications inthe very large classified hierarchy, it is possible to calculatepriorities on classification axes at a high speed.

Second Embodiment

Next, a second embodiment of the present invention will be described.FIG. 7 is a block diagram showing an example of the constitution of adata classifier system according to the second embodiment. As shown inFIG. 7, the present embodiment differs from the first embodiment in thatthe index calculation means 103 shown in FIG. 1 is replaced with asecondary index calculation means 1031.

Specifically, the secondary index calculation means 1031 is configuredof a CPU of an information processing device which operates according toprograms. The secondary index calculation means 1031 has a function ofreceiving (inputting) classification axis candidates from theclassification axis candidate reduction means 1021 and therebycalculating priority on classification axes. In the present embodiment,the secondary index calculation means 1031 calculates priority based onhierarchical depths of classifications, data quantity of classificationsand data redundancy of classifications, or their combinations inaddition to hierarchical distances of classifications.

The term “hierarchical distances of classifications” representsdistances between classifications in the classified hierarchy; hence, itis identical to one described in the first embodiment. In the presentembodiment, an index representing “hierarchical distances ofclassifications” will be referred to as an independence index.

The term “hierarchical depths of classifications” representsshortest/longest path lengths from classifications, serving as basiccategories or roots of classified hierarchy, to other classifications.Classifications having large hierarchical depths are regarded as oneshaving specific semantics.

An example of the classified hierarchy shown in FIG. 8 will be discussedas follows. In the classified hierarchy shown in FIG. 8, the deepestclassifications “family law” and “family member” are regarded asspecific classifications compared to a classification “society”.Specific classifications are deemed useful for users to comprehendconfigurations of data. In the present embodiment, the secondary indexcalculation unit 1031 handles shortest path lengths, from basiccategories to other classifications, as hierarchical depths ofclassifications, so that it calculates a priority with higher values forclassifications having larger hierarchical depths. In the presentembodiment, an index representing “hierarchical depths ofclassifications” will be referred to as a specifics index.

The term “data quantity of classifications” represents the amount ofdata correlated to classifications. The term “data of classifications”represents data directly correlated to classifications or datacorrelated to classifications and their descendant classifications. Inthe present embodiment, the secondary index calculation means 1031employs data correlated to classifications and their descendantclassifications in terms of “data of classifications”. In this case,classifications having larger data quantity are regarded as ones havinghigher exhaustivity. Since classification axes, which are created usingclassifications having high exhaustivity, precisely represent dataaccumulated in the data accumulation unit 203, they are deemed usefulfor users to comprehend the outline of data. In the present embodiment,the secondary index calculation means 1031 calculates priority withhigher values for classifications having larger quantities of data. Inthe present embodiment, an index representing “data quantity ofclassifications” will be referred to as an exhaustivity index.

The term “data redundancy of classifications” indicates valuesrepresenting the degree of correspondence among data on each ofclassifications assigned to classification axes. With a smaller dataredundancy, data accumulated in the data accumulation unit 203 haveuniqueness so that their outline can be easily comprehended. Withredundant data having no uniqueness alone, a plenty of redundant datashould be displayed in connection with classifications, whoseclassification axes cannot be regarded as good classification axes.

When the data accumulation unit 203 accumulates data shown in FIG. 4,for example, a classification “family” is correlated to data whose dataIDs are “d1”, “d2” and “d3”. In addition, a classification “home” isalso correlated to data whose data IDs are “d1”, “d2” and “d3”. Uponadopting a display method for displaying data quantity with respect toeach classification on each classification axis, both theclassifications “family” and “home” are assigned with the same amount ofdata, i.e. three, with the same content; hence, their classificationaxis does not have a significant amount of information. In this case,the secondary index calculation unit 1031 calculates priority whosevalue becomes lower as the data redundancy becomes higher. As the dataredundancy, it is possible to use a value which is produced by dividingthe total amount of data assigned to each classification by the amountof data having no redundancy; alternatively, it is possible to producethe amount of information (entropy) based on the occurrence probabilityof data. In the present embodiment, an index representing the “dataredundancy of classifications” will be referred to as a uniquenessindex.

In the present embodiment, the secondary index calculation unit 1031finally produces the overall priority by use of the aforementionedindexes.

In the present embodiment, the functions of the constituent elementsother than the secondary index calculation unit 1031 are equivalent tothe functions of the counterpart constituent elements described in thefirst embodiment.

Next, the operation will be described. FIG. 9 is a flowchart showing anexample of a data classification process performed by the dataclassifier system of the second embodiment. As shown in FIG. 9, thepresent embodiment differs from the first embodiment in that the processof step S41 for creating a calculation table and the process of step S42for calculating priority on classification axis candidates are executedinstead of the process of step S4 for calculating priority onclassification axis candidates based on hierarchical distances ofclassifications. Hereinafter, the processes different from the firstembodiment will be described.

Similar to the foregoing processes of the first embodiment, the inputmeans 101 of the data classifier system receives the number ofclassifications N according to a user's operation (step S1). Forinstance, the input means 101 inputs N=3 as the number ofclassifications. Next, similar to the foregoing processes of the firstembodiment, the classification axis candidate reduction means 1021obtains (extracts) basic categories of classification axes from thebasic category accumulation unit 202 (step S2). In an example of FIG. 3,the classification axis candidate reduction means 1021 extracts theinformation such as “society”, “nature” and “culture” from the basiccategory accumulation unit 202.

Next, similar to the foregoing processes of the first embodiment, theclassification axis candidate reduction means 1021 reduces the number ofclassification axes which are produced based on data quantity ofclassifications and hierarchical distances of classifications (stepS31).

Next, similar to the foregoing processes of the first embodiment, theclassification axis candidate reduction means 1021 obtains (extracts)classifications, which are each correlated to the specific amount ofdata or more, within classifications descendant from each basic categorywith reference to the information stored in the classified hierarchyaccumulation unit 201 and the information stored in the dataaccumulation unit 203. Subsequently, the classification axis candidatereduction means 1021 creates classification axis candidates by use ofcombinations, the number of which is equal to the number of extractedclassifications (step S32). However, the classification axis candidatereduction means 1021 does not create classification axis candidatesincluding classifications belonging to the ancestor-child relationship.

Next, the secondary index calculation means 1031 obtains (inputs)classification axis candidates from the classification axis candidatereduction means 1021, thus creating a calculation table with referenceto the information stored in the classified hierarchy accumulation unit201 and the information stored in the data accumulation unit 203 (stepS41). Herein, the calculation table is a temporary table created for thepurpose of calculating indexes; hence, the secondary index calculationmeans 1031 creates two tables, namely a classification-specified dataquantity table and a data-specified classification number table.

The classification-specified data quantity table is a table counting theamount of data correlated to each classification, wherein it includesrecords correlated with classifications, data quantity and depths ofclassifications. The data-specified classification number table is atable counting the number of classifications on each classification axiscorrelated to each data, wherein it includes data IDs andclassifications. In this connection, it is preferable that the secondaryindex calculation means 1031 extend tables, temporarily created for thepurpose of calculations, on memory.

FIG. 10(A) and FIG. 10(B) show an example of a classification-specifieddata quantity table and an example of a data-specified classificationnumber table. Herein, FIG. 10(A) shows an example of theclassification-specified data quantity table. FIG. 10(B) shows anexample of the data-specified classification number table. As shown inFIG. 10(A) and FIG. 10(B), the secondary index calculation means 1031creates the classification-specified data quantity table and thedata-specified classification number table based on classification axes(society: family, diplomacy, medical care), the information of FIG. 2stored in the classified hierarchy accumulation unit 201 and theinformation of FIG. 4 stored in the data accumulation unit 203.

In FIG. 10(A), the classification-specified data quantity table is atable including records correlated with classifications, data quantityand depths of classifications. In the present embodiment, depths ofclassifications represent path lengths from each basic category toclassifications. With reference to the information of FIG. 4 stored inthe data accumulation unit 203, for example, the classification “family”is correlated to “d1”, “d2” and “d3”; hence, as shown in FIG. 10(A), theamount of data is three. With reference to the information of FIG. 2stored in the classified hierarchy accumulation unit 201, there are twodepths lying from the basic category “society” to the classification“family” via “living”.

In the present embodiment, the correlation between classifications anddata is defined as data directly correlated to classifications anddescendant classifications. For instance, no data is directly correlatedto the classification “medical care”: hence, it is necessary to checkdata directly correlated to its descendant classification. Herein, thedescendant classification “medicine” or “health care” is correlated todata IDs of “d2”, “d4” and “d6”. Thus, as shown in FIG. 10(A), theamount of data is set to three.

The data-specified classification number table is a table includingrecords correlated with data ID and the number of classifications. Inthe present embodiment, the data-specified classification number tableshown in FIG. 10(B) describes the number of classifications assigned toa classification axis (society: family, diplomacy, medical care) withrespect to each data ID. With reference to the information of FIG. 4stored in the data accumulation unit 203, for example, the data ID “d1”is correlated to the classification “family” on each classification axisso that the number of classifications is one as show in FIG. 10(B). Inaddition, the data ID “d6” is correlated to a classification descendantfrom the classification “medical care”, so that the number ofclassifications is one as shown in FIG. 10(B).

Next, the secondary index calculation means 1031 calculates priority onclassification axes by use of the calculation table (step S42). In thepresent embodiment, the secondary index calculation means 1031calculates indexes of independence, specifics, exhausitivity anduniqueness so as to produce a linear addition of these indexes, thuscalculating the overall priority by use of Equation (4).

Priority(X:C)=W1×Independence(X:C)+W2×Specifics(X:C)+W3×Exhausitivity(X:C)+W4×Uniqueness(X:C)  (4)

In Equation (4), X denotes basic categories, and C denotesclassifications. In addition, W1, W2, W3 and W4 denote weightcoefficients to indexes. These weight coefficients can be set to thesystem in advance (e.g. preset values can be stored in a storage unitsuch as a memory in advance), or they can be set by users. In thepresent embodiment, weight coefficients are set to the system inadvance.

The present embodiment is equivalent to the first embodiment in terms ofthe independence index; hence, the secondary index calculation means1031 produces it according to Equation (5).

Independence(X:C)=1/Max(X)×1/(2×number ofcombinations)×ΣComDist(ci,cj)  (5)

In Equation (5), X, C, Max(X), the number of combinations andComDist(ci,cj) are identical to those described in the first embodiment.

In addition, the secondary index calculation means 1031 calculates thespecifics index according to Equation (6). Herein, the specifics indexrepresents an average value of path lengths from each basic category toclassifications on each classification axis.

Specifics(X:C)=1/Max(X)×1/N×ΣDepth(X,ci)  (6)

In Equation (6), Max(X) represents the maximum depth amongclassifications descendant from the basic category X. In addition, Nrepresents the number of classifications given (input) by the inputmeans 101. Furthermore, Depth(X,ci) represents the shortest path lengthfrom the basic category X to the classification ci. Herein, the averagepath length needs to be divided by Max(X) because each basic category islinked to descendant classifications having different depths. Thesecondary index calculation means 1031 is able to calculate thespecifics index according to Equation (7) by use of theclassification-specified data quantity table.

Specifics(X:C)=1/Max(X)×1/N×ΣDepth(X,ci)=1/Max(X)×1/N×Σ(depths ofclassifications in the classification-specified data quantitytable)  (7)

Equation (7) shows that specifics indexes become high as depths ofclassifications become large.

The secondary index calculation means 1031 calculates the exhaustivityindex according to Equation (8). Herein, the exhaustivity index is acover ratio of data of each classification to all data.

Exhaustivity(X:C)=1/DataNum×|U Data(ci)|  (8)

In Equation (8), DataNum denotes the amount of data subjected toclassification. Data (ci) denotes a set of data allocated to aclassification ci. In addition, “U Data (ci)” denotes a sum set of dataranging from a classification c1 to a classification cN on eachclassification axis. Furthermore, |U Data (ci)| denotes the number ofelements within a set of data ranging from the classification c1 to theclassification cN on each classification axis. That is, |U Data (ci)|denotes the amount of data allocated to classifications. The secondaryindex calculation means 1031 is able to calculate the exhaustivity indexaccording to Equation (9) using the previously created data-specifiedclassification number table.

Exhaustivity(X:C)=1/DataNum×|U Data(ci)|=1/DataNum×RecNum(data-specifiedclassification number table, number of classifications>0)  (9)

In Equation (9), RecNum (data-specified classification number table,number of classifications>0) denotes the number of records with thenumber of classifications greater than zero within the data-specifiedclassification number table. This term of RecNum (data-specifiedclassification number table, number of classifications>0) is equal tothe amount of data allocated to classifications. Therefore, it can berewritten as shown in Equation (9).

The secondary index calculation means 1031 calculates the uniquenessindex according to Equation (10). Herein, the data redundancy is a valuewhich is produced by dividing the total amount of data allocated to eachclassification by the amount of data having no redundancy. In thisconnection, the uniqueness index is expressed as the inverse of the dataredundancy.

Uniqueness(X:C)=1/(1/|U Data(ci)|×ΣCatNum(ci))  (10)

In Equation (10), |U Data (ci)| denotes the amount of data having noredundancy allocated to each classification. In addition, CatNum(ci)denotes the amount of data allocated to the classification ci.Furthermore, ΣCatNum(ci) denotes the total amount of data ranging fromthe classification c1 to the classification cN on each classificationaxis. The secondary index calculation means 1031 is able to calculatethe uniqueness index according to Equation (11) using the previouslycreated classification-specified data quantity table.

Uniqueness(X:C)=1/(1/|U Data(ci)|×ΣCatNum(ci))=1/(RecNum(data-specifiedclassification number table, number of classifications>0)×Σ(amount ofdata in data-specified classification number data)  (11)

In the case of a classification axis (society: family, diplomacy,medical care), for example, the secondary index calculation means 1031calculates the aforementioned indexes according to Equation (12) throughEquation (15) with reference to the tables shown in FIG. 10(A) and FIG.10(B) and the information of FIG. 2 stored in the classified hierarchyaccumulation unit 201.

Independence(X:C)=1/Max(X)×1/(2×number ofcombinations)×ΣComDist(C1,C2)=1/Max(society)×1/(2×3)×(ComDist(family,diplomacy)+ComDist(family, medical care)+ComDist(diplomacy, medicalcare))=½×⅙×(4+4+4)=1  (12)

Specifics(X:C)=1/Max(X)×1/N×(depths of classifications inclassification-specified data quantitytable)=1/Max(society)×⅓×(2+2+1)=½×⅓×(2+2+1)=0.833  (13)

Exhaustivity(X:C)=1/DataNum×RecNum(data-specified classification numbertable, number of classifications>0)=⅙×6=1  (14)

Uniqueness(X:C)=1/(RecNum(data-specified classification number table,number of classifications>0)×Σ(amount of data in data-specifiedclassification number table))=1/(⅙×(3+2+3))= 6/8=0.75  (15)

When all the weight coefficients are set to 0.25, the secondary indexcalculation means 1031 is able to calculate priority according toEquation (16).

Priority(X:C)=W1×Independency(X:C)+W2×Specifics(X:C)+W3×Exhaustivity(X:C)+W4×Uniqueness(X:C)=0.25×1+0.25×0.833+0.25×1+0.25×0.75=0.895=0.90  (16)

Next, similar to the foregoing processes of the first embodiment, theoutput means 104 outputs classification axes, priority and data (stepS5).

As described above, the present invention calculates priority based onhierarchical depths of classifications, data quantity of classificationsand data redundancy of classifications, or their combinations inaddition to hierarchical distances of classifications. For this reason,it is possible to effectively reduce the calculation time of priority onclassification axes in light of hierarchical depths of classifications,data quantity of classifications and data redundancy of classificationsin addition to hierarchical distances of classifications.

Third Embodiment

Next, a third embodiment of the present invention will be described.FIG. 11 is a block diagram showing an example of the constitution of adata classifier system according to the third embodiment. As shown inFIG. 11, the present embodiment differs from the second embodiment inthat the data classifier system further includes a display means inaddition to the constituent elements shown in FIG. 7.

Specifically, the display means 105 is configured of a CPU of aninformation processing device which operates according to programs and adisplay device such as a display. The display means 105 has a functionof outputting (displaying) classification axes, priority and datacalculated by the secondary index calculation means 1031 to a displaydevice such as a display. For instance, the display means 105 outputs(displays) the amount of data allocated to each classification on eachclassification axis, data and attributes in a list form or a table form.

First, the operation of the display means 105 displaying the informationin a list form will be described. FIG. 12 shows an example of theinformation which the display means 105 displays in a list form. Asshown in FIG. 12, the display means 105 displays a screen imageincluding display areas representing a classification axis, aclassification axis candidate list and a data list. As theclassification axis, the display means 105 displays a classificationaxis having the highest priority or a classification axis selected fromamong the classification axis candidate list. In addition, the displaymeans 105 displays basic categories and classifications each followed bythe amount of data.

FIG. 12 shows an example using a classification axis (society: family,health care, transplant). In this case, the display means 105 obtains(extracts) the amount of data allocated to each classification from thedata accumulation unit 203.

In an example of FIG. 12, a column indicative of an item “other” isnewly added to the screen image displayed on the display means 105. Thecolumn of “other” below “society” is connected with the basic categoryof “society” but is defined as a classification which differs fromclassifications of “family”, “health care” and “transplant”. The columnof “other” juxtaposed with the basic category of “society” is defined asa classification irrelevant to the basic category of “society”. In thisconnection, the display means 105 is able to obtain the amount of dataallocated to the column of “other” with reference to the dataaccumulation unit 203. As the amount of data, the present embodimentfurther includes the amount of data correlated to descendantclassifications.

In an example of FIG. 12, the display means 105 displays classificationaxes, whose priorities have been calculated, in an order of prioritiesin the classification axis candidate list. The display means 105 is ableto obtain these classification axes based on the calculation result ofthe secondary index calculation means 1031.

In an example of FIG. 12, the display means 105 displays various data inthe data list. In this case, the display means 105 displays those datacorrelated with data IDs, detailed content and counterpartclassifications. The display means 105 is able to obtain informationregarding displayed data with reference to the information stored in thedata accumulation unit 203.

In the present embodiment, the functions of the constituent elementsother than the display means 105 are equivalent to the functions of thecounterpart constituent elements described in the second embodiment.

Next, the operation of the display means 105 for displaying informationin a list form will be described. As an initial rendition, the displaymeans 105 displays a classification axis having the highest priority inthe classification axis display area. In addition, the display means 105displays other classification axis candidates in the classification axiscandidate list in an order of priorities. Furthermore, the display means105 displays all data accumulated in the data accumulation unit 203 inthe data list.

Next, when an operation is made to select any one of classifications orbasic categories from among classification axes displayed in theclassification axis display area, the display means 105 displays thecorresponding data in the data list. When an operation is made to selectany one of classification axis candidates displayed in the areadisplaying the classification axis candidate list, the display means 105changes the information of the counterpart classification axis displayportion with the selected classification axis.

Next, the operation of the display means 105 for displaying informationin a table form will be described. FIG. 13 shows an example of theinformation which the display means 105 displays in a table form. Asshown in FIG. 13, display means 105 renders various areas including aclassification table, a data list and a classification axis candidatelist on the screen image.

The display means 105 displays a classification axis having the highestpriority among classification axis candidates on the horizontal axis ofthe classification table. In addition, the display means 105 displaysrelevant attributes on the vertical axis. In the present embodiment, thedisplay means 105 displays classifications as attributes. This exampleis illustrative; hence, the display means 105 can display creators ofdata as attributes. A plurality of attributes can be each selected anddisplayed by way of the user's operation. The display means 105 displaysthe information showing what kind of data exists in each cell of thetable. In the present embodiment, the display means 105 displays dataIDs and the amounts of data.

Next, the horizontal axis of “other” will be described. In theclassification table shown in FIG. 13, “other” below the basic categoryof “society” is defined as a classification showing data groups whichare not correlated to classifications on each classification axis belowthe basic category. In FIG. 13, “other” at the rightmost position isdefined as a classification showing data groups which are not correlatedto the basic category of “society”. An item of “other” on the verticalaxis shows data groups, which are not correlated to the displayed onesamong relevant attributes.

Hereinafter, a procedure for displaying attributes relevant to thevertical axis will be described. First, the display means 105 obtains(extracts) data groups correlated to classification axes with referenceto the information stored in the classified hierarchy accumulation unit201 and the information stored in the data accumulation unit 203. Next,the display means 105 checks (obtains) data quantities relevant to eachattribute with reference to the obtained (extracted) attributes of datagroups. The display means 105 displays data quantities, the number ofwhich corresponds to the number of classifications on eachclassification axis, on the vertical axis in an order counting from thelargest data quantity.

In the present embodiment, the display means 105 identifiesclassifications below the basic category as attributes so as to obtain(calculate) data quantities allocated to those classifications. Thedisplay means 105 further displays the information representingclassification axes other than the already displayed one. Specifically,a classification axis (society: family, health care, transplant) iscorrelated to data “d1”, “d2”, “d3”, “d4” and “d6”. As theclassifications having large data quantities, which are correlated tothese data but not allocated to the classification axis, theclassification of “living” embraces four items (“d1”, “d2”, “d3” and“d4”); the classification of “home” embraces three items (“d1”, “d2” and“d3”); the classification of “medical care” embraces three items (“d2”,“d4” and “d6”); and the classification of “medicine” embraces threeitems (“d2”, “d4” and “d6”).

In the above case, the display means 105 selects the classifications,each having three items, in an order of ones having larger dataquantity. The display means 105 selects and displays either one ofclassifications both having the same data quantity. This example isillustrative; hence, the display means 105 does not necessarily selectclassifications as attributes but can select and display otherinformation. For instance, the display means 105 can select and displayany attributes ascribed to data upon the user's operation. The displaymeans 105 can automatically select and determine attributes according tothe above procedure; alternatively, attributes can be selected upon theuser's operation. In FIG. 13, the number of attributes displayed on thevertical axis is not necessarily equal to the number of classificationaxes.

When any one of cells of the classification table is selected, thedisplay means 105 displays a data list corresponding to the selectedcell. In the present embodiment, the display means 105 displays the dataID, the content and the classification. The display means 105 displaysthese pieces of information with reference to the information stored inthe data accumulation unit 203.

The display means 105 displays classification axes, whose prioritieshave been calculated, in the classification axis candidate list in anorder of priorities. The display means 105 is able to obtain thesepieces of information based on the calculation result of the secondaryindex calculation means 1031.

Next, the operation of the display means 105 for displaying informationin a table form will be described. As an initial rendition, the displaymeans 105 displays a classification axis having the highest priority onthe horizontal axis of the classification table. In this case, thedisplay means 105 further displays relevant attributes constituting thevertical axis in accordance with the foregoing method. In thisconnection, the display means 105 does not display any data in the datalist.

Next, when any one of cells in the classification table is selected uponthe user's operation, the display means 105 displays data correspondingto the selected cell in the data list.

Next, when any one of classification axes in the classification axiscandidate list is selected upon the user's operation, the display means105 displays the selected classification axis serving as the horizontalaxis. In this case, the display means 105 newly displays relevantattributes on the vertical axis of the classification table.

According to the present embodiment described above, the classificationaxis, priority and data produced by the secondary index calculationmeans 1031 are displayed in a list form or in a table form. This allowsusers to visually recognize the selected status of classification axes,priority and data.

Fourth Embodiment

Next, a fourth embodiment of the present invention will be described.FIG. 14 is a block diagram showing an example of the constitution of adata classifier system according to the fourth embodiment. As shown inFIG. 14, the present embodiment differs from the first embodiment inthat the data classifier system includes a multidimensionalclassification axis candidate creation means 1023 in addition to theconstituent elements shown in FIG. 1. In addition, the presentembodiment differs from the first embodiment in that the data classifiersystem replaces the index calculation means 103 with a multidimensionalindex calculation means 1032.

In the present embodiment, the classification axis candidate reductionmeans 1021 does not combine all the classifications descendant from eachbasic category in accordance with a procedure similar to the foregoingprocedure of the first embodiment, but it reduces the number ofclassification axes which are created based on data quantity ofclassifications and hierarchical distances of classifications. Thus, theclassification axis candidate reduction means 1021 is able to reduce thenumber of classification axes subjected to priority calculation, thusachieving high-speed calculation.

Specifically, the multidimensional classification axis candidatecreation means 1023 is configured of a CPU of an information processingdevice which operates according to programs. The multidimensionalclassification axis candidate creation means 1023 implements functionsof receiving (inputting) classification axis candidates from theclassification axis candidate reduction means 1021 and combining aplurality of classification axis candidates, thus creatingmultidimensional classification axes. In this connection, the number ofdimensions applied to the created multidimensional classification axescan be set to the system in advance (e.g. preset values can be stored ina storage unit such as a memory in advance); alternatively, they can beinput upon the user's operation. In addition, the multidimensionalclassification axis candidate creation means 1023 implements a functionof transferring (outputting) the created multidimensional classificationaxes to the multidimensional index calculation means 1032.

When the number of dimensions is two, for example, the multidimensionalclassification axis candidate creation means 1023 createsmultidimensional classification axis candidates each combining twoclassification axes. In this case, the multidimensional classificationaxis candidate creation means 1023 creates a multidimensionalclassification axis (society: home, family, health care)−(society:diplomacy, medicine, transplant) or the like.

Hereinafter, each multidimensional classification axes created by themultidimensional classification axis candidate creation means 1023 willbe expressed using a notation of (basic category: Nclassifications)−(basic category: N classifications). With respect tomultidimensional classification axes each having three dimensions ormore, the above notation is followed by new classification axes with asymbol of “−” therebetween, thus expressing multidimensionalclassification axes. In this case, each of classification axes connectedwith a symbol of “−” therebetween denotes a classification in eachdimension. In the case of the multidimensional classification axis(society: home, family, health care)−(society: diplomacy, medicine,transplant), for example, the first classification axis (society: home,family, health care) designates a first-dimensional classification axiswhilst the second classification axis (society: diplomacy, medicine,transplant) designates a second-dimensional classification axis.

Specifically, the multidimensional index calculation means 1032 isconfigured of a CPU of an information processing device which operatesaccording to programs. The multidimensional index calculation means 1032implements functions of receiving (inputting) multidimensionalclassification axis candidates from the multidimensional classificationaxis candidate creation means 1023 and calculating priority onmultidimensional classification axis candidates with reference to theinformation stored in the classified hierarchy accumulation unit 201 andthe information stored in the data accumulation unit 203. In this case,the multidimensional index calculation means 1032 calculates prioritybased on hierarchical distances of classifications in the classifiedhierarchy.

The term “hierarchical distances of classifications” representsshortest/longest path lengths leading to common ancestors orshortest/longest path lengths leading to common descendants. Aspriority, the multidimensional index calculation means 1032 calculatesaverage values or maximum/minimum values of hierarchical distances ofclassifications on classification axes.

In the present embodiment, the multidimensional index calculation means1032 employs shortest path lengths leading to common ancestors as“hierarchical distances of classifications” so as to calculate averagevalues of hierarchical distances as priority. This is becauseclassifications having longer hierarchical distances can be regarded assemantically independent ones. In addition, the multidimensional indexcalculation means 1032 calculates priority based on hierarchicaldistances of basic categories on classification axes in addition tohierarchical distances of classifications on classification axes.

In the present embodiment, the constituent elements other than themultidimensional classification axis candidate creation means 1023 andthe multidimensional index calculation means 1032 are equivalent to thecounterpart constituent elements described in the first embodiment.

Next, the operation will be described. FIG. 15 is a flowchart showing adata classification process performed by the data classifier system ofthe fourth embodiment.

First, similar to the foregoing process described in the firstembodiment, the input means 101 of the data classifier system receivesthe number of classifications N upon the user's operation (step S1). Forinstance, the input means 101 inputs N=3 as the number ofclassifications. Next, similar to the foregoing process described in thefirst embodiment, the classification axis candidate reduction means 1021obtains (extracts) basic categories of classification axes from thebasic category accumulation unit 202 (step S2). In an example of FIG. 3,the classification axis candidate reduction means 1021 extracts theinformation representative of “society”, “nature” and “culture” from thebasic category accumulation unit 202.

Next, similar to the foregoing process described in the firstembodiment, the classification axis candidate reduction means 1021reduces the number of classifications based on data quantity ofclassifications with reference to the information stored in theclassified hierarchy accumulation unit 201 and the information stored inthe data accumulation unit 202 (step S31).

Next, similar to the foregoing process described in the firstembodiment, the classification axis candidate reduction means 1021creates classification axes each combining classifications based onhierarchical distances of classifications with reference to theinformation stored in the classified hierarchy accumulation unit 201(step S32).

Next, the multidimensional classification axis candidate creation means1023 creates multidimensional classification axes each combining thecreated classification axis candidates, the number of which correspondsto the number of dimensions (step S321). In this connection, the numberof dimensions for multidimensional classification axes can be set to thesystem in advance preset values can be stored in a storage device suchas a memory in advance); alternatively, they can be input upon theuser's operation. When the number of dimensions is two, for example, themultidimensional classification axis candidate creation means 1023creates a multidimensional classification axis (society: home, family,health care)−(society: diplomacy, medicine, transplant).

Next, the multidimensional index calculation means 1032 obtains (inputs)multidimensional classification axis candidates from themultidimensional classification axis candidate creation means 1023 so asto calculate priority on each multidimensional classification axis withreference to the information stored in the classified hierarchyaccumulation unit 201 (step S421).

For the purpose of securing an independence of semantics in prioritycalculation, the multidimensional index calculation means 1032calculates average values of hierarchical distances of classificationsand average values of hierarchical distances of basic categories.Herein, “hierarchical distances of classifications” or “hierarchicaldistances of basic categories” represent shortest paths ofclassifications leading to ancestor classifications. Themultidimensional index calculation means 1032 calculate priorityaccording to Equations (17) and (18).

Multidimensional Priority((X1:C1)−(X2:C2)− . . . )=1/number ofdimensions×ΣIndependence(Xi:Ci)+1/(2×number ofdimensions)×ΣComDist(Xi,Xj)  (17)

Independent(X:C)=1/Max(X)×1/(2×number ofcombinations)×ΣComDist(ci,cj)  (18)

In Equation (17), X1, X2, . . . , Xi denote basic categories in adimension i. In addition, C1, C2, . . . , Ci denote classifications in adimension i. In this connection, Max(X) and ComDist(ci,cj) areequivalent to the foregoing ones described in the first embodiment.According to Equation (17), the multidimensional index calculation means1032 calculates an average value by dividing the independence (i.e. ahierarchical distance between classifications), which is calculated inthe first term with respect to each dimension, by the number ofdimensions. In addition, the multidimensional index calculation means1032 calculates an average value of hierarchical distances of basiccategories in the second term.

For instance, the multidimensional index calculation means 1032calculates priority on the multidimensional classification axis(society: home, family, health care)−(society: diplomacy, medicine,transplant) according to Equation (19). In this case, the number ofcombinations is set to three because of the number of classificationsN=3, whilst the number of dimensions is set to two. The deepestclassification among classifications descendant from the basic categoryof “society” is 2 in the classified hierarchy shown in FIG. 2.

Priority((society: home, family, health care)−(society: diplomacy,medicine, transplant))=½(½×⅙×(ComDist(home, family)+ComDist(home,healthcare)+ComDist(family, health care))+(½×⅙×(ComDist(diplomacy,medicine)+ComDist(diplomacy, transplant)+ComDist(medicine,transplant))+¼×(ComDist(society,society))=½×(½×⅙×(2+2+2)+(½×⅙×(4+4+2))+¼×(0)=0.67  (19)

In the case of multidimensional classification axes of three dimensionsor more, however, the multidimensional index calculation means 1032 isable to calculate multidimensional priority by way of calculationsequivalent to the foregoing ones.

According to the above calculations, it is possible to impart a highpriority to classification axes including semantically independentclassifications in addition to similar classifications. In addition, itis possible to cope with multidimensional classification axes.

Next, the output means 104 outputs classification axes, priority anddata based on the calculation result of the multidimensional indexcalculation means 1032 (step S5). FIG. 16(A), FIG. 16(B) and FIG. 16(C)show examples of the information output from the output means 104 of thefourth embodiment. In examples of FIG. 16(A), FIG. 16(B) and FIG. 16(C),the output means 104 outputs three tables. In the illustrations, “ . . .” denotes an omission mark.

For instance, the output means 104 outputs a table of FIG. 16(A)including records correlated with the dimensional ID, the classificationaxis ID and the score. That is, the table of FIG. 16(A) describesclassification axes and their scores per each multidimensionalclassification axis candidate. In an example of FIG. 16(A),classification axis IDs are each described with a delimiter of “,”therebetween. Since the number of dimensions is set to two in thepresent embodiment, classification axis IDs include two classificationaxis IDs. In the case of multidimensional classification axes of threedimensions or more, it is possible to cope with multiple dimensions byincreasing the number of classification axis IDs.

In addition, the output means 104 outputs a table of FIG. 16(B)including records correlated with the classification ID, the basiccategory and the classification. In an example of FIG. 16(B), one rowrepresents one classification axis.

Furthermore, the output means 104 outputs a table of FIG. 16(C)including records correlated with the classification ID, theclassification and the data ID. In an example of FIG. 16(C), one recordcorresponds to classifications on each classification axis. In anexample of FIG. 16(C), data IDs are each described with a delimiter of“,” therebetween. In the illustration, “ . . . ” denotes an omissionmark.

The output methods of FIG. 16(A), FIG. 16(B) and FIG. 16(C) areillustrative; hence, the output means 104 can outputs a single tablecombining two tables, or it can additionally output a new tableincluding attribute information of each data.

According to the aforementioned constitution, it is possible to selectclassification axes based on the semantic independence betweenclassifications. Thus, it is possible to select classification axescomprehensible for users.

As described above, when creating classification axes based on thecombination of classifications, assigned to at least one data, amongclassifications descendant from each basic category, the presentembodiment is able to reduce the number of classification axiscandidates subjected to calculations based on data quantity ofclassifications and hierarchical distances of classifications. Thepresent embodiment combines the reduced classification axis candidatesso as to create multidimensional classification axis candidates. Inaddition, the present embodiment calculates priority on multidimensionalclassification axis candidates based on hierarchical distances ofclassifications in the classified hierarchy. Therefore, upon receivingdata groups correlated to the classified hierarchy and classifications,it is possible to reduce priority calculation times on multidimensionalclassification axes as well.

Fifth Embodiment

Next, a fifth embodiment of the present invention will be described.FIG. 17 is a block diagram showing an example of the constitution of adata classifier system according to the fifth embodiment. As shown inFIG. 17, the present embodiment differs from the fourth embodiment inthat the data classifier system includes a secondary multidimensionalindex calculation means 1033 instead of the multidimensional indexcalculation means 1032 shown in FIG. 14.

Specifically, the secondary multidimensional index calculation means1033 is configured of a CPU of an information processing device whichoperates according to programs. The secondary multidimensional indexcalculation means 1033 has functions of receiving (inputting)classification axis candidates from the multidimensional classificationaxis candidate creation means 1022 and calculating priority onclassification axes. In this case, the secondary multidimensional indexcalculation means 1033 calculates priority based on hierarchical depthsof classifications, data quantity of classifications and data redundancyof classifications, or their combinations in addition to hierarchicaldistances of classifications.

As a priority calculation method, the secondary multidimensional indexcalculation means 1033 employs a method extending the prioritycalculation method of the second embodiment in a multidimensionalmanner, thus calculating priority.

In the present embodiment, the functions of the constituent elementsother than the secondary multidimensional index calculation means 1033are equivalent to the functions of the counterpart constituent elementsdescribed in the fourth embodiment.

Next, a priority calculation method will be described in connection withthe secondary multidimensional index calculation means 1033 calculatingpriority. According to the procedure similar to that of the secondembodiment, the secondary multidimensional index calculation means 1033obtains (inputs) multidimensional classification axis candidates fromthe multidimensional classification axis candidate creation means 1023so as to create a calculation table with reference to the informationstored in the classified hierarchy accumulation unit 201 and theinformation stored in the data accumulation unit 203. The presentembodiment differs from the second embodiment in that the secondarymultidimensional index calculation means 1033 creates a calculationtable over multiple dimensions.

As the calculation table, the secondary multidimensional indexcalculation means 1033 creates two tables, namely aclassification-specified data quantity table and a data-specifiedclassification number table.

The classification-specified data quantity table is a table counting theamount of data correlated to combinations of classifications in multipledimensions. The classification-specified data quantity table includesrecords correlated with combinations of classifications, data quantityand depths of classifications. The data-specified classification numbertable is a table counting the number of classifications on eachclassification axis corresponding to each data, wherein it includes dataIDs and combinations of classifications. It is preferable that thesecondary multidimensional index calculation means 1033 creates atemporary calculation table in memory.

FIG. 18(A) and FIG. 18(B) show an example of theclassification-specified data quantity table and an example of thedata-specified classification number table. Herein, FIG. 18(A) shows anexample of the classification-specified data quantity table. FIG. 18(B)shows an example of the data-specified classification number table. Inthese examples shown in FIGS. 18(A) and FIG. 18(B), the secondarymultidimensional index calculation means 1033 creates theclassification-specified data quantity table and the data-specifiedclassification number table based on a multidimensional classificationaxis (society: home, family, health care)−(society: diplomacy, medicine,transplant), the information of FIG. 2 stored in the classifiedhierarchy accumulation unit 201 and the information of FIG. 4 stored inthe data accumulation unit 203.

In an example of FIG. 10(A), the classification-specified data quantitytable is a table including records correlated with combinations ofclassifications, data quantity and depths of classifications. For thepurpose of simplifying subsequent calculations, the secondarymultidimensional index calculation means 1033 of the present embodimentcalculates depths of classifications according to Equation (20).

Depth(cij,ckl, . . . )=1/number ofdimensions×Σ(1/Max(Xi)×Depth(Xi,cij))  (20)

In Equation (20), cij denotes classifications i and j, and clk denotesclassifications k and l. Herein, values i and k differ each other. Inaddition, Xi denotes an i-dimensional basic category. Max(Xi) denotes adepth of a deepest classification among classifications descendant fromthe basic category Xi. Depth(Xi,cij) denotes a shortest path length fromthe i-dimensional basic category to the classification cij. Furthermore,a symbol Σ denotes the summation with combinations of classifications indifferent dimensions. In Equation (20), depths of classifications aredefined as average values of depths of classifications amongcombinations of classifications.

In FIG. 18(A), for example, a first record represents data correlated toa first-dimensional classification of “home” and a second-dimensionalclassification of “diplomacy”. With reference to the information of FIG.4 stored in the data accumulation unit 203, the data accumulation unit203 does not store data correlated to these two classifications; hence,the amount of data is zero as shown in FIG. 18(A). As to depths ofclassifications, two depths exist from society to family; two depthsexist from society to medicine; two depths exist in Max(Xi); and thenumber of dimensions is two; hence, the depth of the classification isset to I as shown in FIG. 18(A).

The data-specified classification number table is a table includingrecords correlated to data IDs and combinations of classifications. FIG.18(B) shows an example of the data-specified classification number tabledescribing the number of classifications correlated to amultidimensional classification axis (society: home, family, healthcare)−(society: diplomacy, medicine, transplant) with respect to eachdata ID. In an example of FIG. 18(B), a data ID “d2” is correlated tothe first-dimensional classification of “health care” and thesecond-dimensional classification of “medicine” with reference to theinformation of FIG. 4 stored in the data accumulation unit 203; hence,the number of classifications is set to 1.

Next, the secondary multidimensional index calculation means 1033calculates priority on classification axes by use of the calculationtable. In the present embodiment, the secondary multidimensional indexcalculation means 1033 calculates the aforementioned independence index,specifics index, exhaustivity index and uniqueness index so as to obtaina linear addition of these indexes with weights, thus calculating theoverall priority according to Equation (21).

Multidimensional Priority((X1:C1)−(X2:C2)− . . . )=W1×MultidimensionalIndependence((X1:C1)−(X2:C2)− . . . )+W2×MultidimensionalSpecifics((X1:C1)−(X2:C2)− . . . )+W3×MultidimensionalExhausitivity((X1:C1)−(X2:C2)− . . . )W4×MultidimensionalUniqueness((X1:C1)−(X2:C2)− . . . )  (21)

In Equation (21), X denotes a basic category, and C denotesclassifications. In addition, W1, W2, W3 and W4 denote weightcoefficients to respective indexes. In this connection, these weightcoefficients can be set to the system in advance (e.g. preset values canbe stored in a storage unit such as a memory in advance); alternatively,they can be set upon the user's operation. In the present embodiment,these weight coefficients have been set to the system in advance.

The present embodiment is equivalent to the fifth embodiment in terms ofthe independence index, wherein the secondary multidimensional indexcalculation means 1033 calculates multidimensional independence indexesaccording to Equations (22) and (23).

Multidimensional Independence((X1:C1)−(X2:C2)− . . . )=1/number ofdimensions×ΣIndependence(Xi:Ci)+1/(2×number ofdimensions)×ΣComDist(Xi,Xj)  (22)

Independence(X:C)=1/Max(X)×1/(2×number ofcombinations)×ΣComDist(C1,C2)  (23)

In Equations (22) and (23), X1, X2, X1, C1, C2, Ci, Max(X) and ComDistas well as the number of combinations and the number of dimensions areequivalent to those described in the fourth embodiment.

The secondary multidimensional index calculation means 1033 calculatesspecifics indexes according to the following calculations. In thepresent embodiment, specifics indexes are average values of path lengthsfrom basic categories to classifications on classification axes. Thesecondary multidimensional index calculation means 1033 is able tocalculate specifics indexes according to Equations (24) and (25) by useof the classification-specified data quantity table.

Multidimensional Specifics((X1:C1)−(X2:C2)− . . . )=1/number ofdimensions×ΣSpecifics(Xi:Ci)  (24)

Specifics(X:C)=1/Max(X)×1/N×ΣDepth(X,cj)  (25)

In Equations (24) and (25), Max(X), N and Depth (X,cj) are equivalent tothose described in the second embodiment. As shown in FIG. 18(A), depthsof classifications in the classification-specified data quantity tablehave been already calculated according to 1/number ofdimensions×Σ1/Max(Xi)×Depth(Xi,cij); hence, it is possible to calculatemultidimensional specifics indexes according to Equation (26).

Multidimensional Specifics((X1:C1)−(X2:C2)− . . . )=1/number ofdimensions×1/N×ΣΣ(1/Max(Xi)×Depth(Xi,cij))=1/(N dimensions)×Σ(depths inclassification-specified data quantity table)  (26)

The secondary multidimensional index calculation means 1033 calculatesexhaustivity indexes according to the following calculation. In thepresent embodiment, the exhaustivity index is a cover ratio to all datacorrelated to combinations of classifications in each dimension. Thesecondary multidimensional index calculation means 1033 is able tocalculate exhaustivity indexes according to Equation (27) by use of thepreviously created data-specified classification number table.

Multidimensional Exhaustivity((X1:C1)−(X2:C2)− . . . )=1/DataNum×|UData(cij,ckl, . . . )|=1/DataNum×RecNum(data-specified classificationnumber table, number of classifications>0)  (27)

In Equation (27), “Data (cij,ckl, . . . )” denotes a set of all datacorrelated to an i-dimensional j classification cij, a k-dimensional Iclassification ckl, and classifications of other dimensions. Inaddition, DataNum denotes the number of data sets. RecNum(data-specified classification number table, number ofclassifications>0) denotes the number of records each having zero ormore classifications in the data-specified classification number table.This RecNum (data-specified classification number table, number ofclassifications>0) is equivalent to the amount of data correlated tocombinations of classifications. Therefore, it is possible to rewritethe foregoing equation as Equation (27).

The secondary multidimensional index calculation means 1033 calculatesuniqueness indexes according to the following calculation. In thepresent embodiment, the uniqueness index is defined as the inverse ofthe data redundancy. Herein, the data redundancy is a value which isproduced by dividing the total quantity of data correlated tocombinations of classifications by the total quantity of data having noredundancies. The secondary multidimensional index calculation means1033 is able to calculate uniqueness indexes according to Equation (28)by use of the previously created classification-specified data quantitytable.

Multidimensional Uniqueness((X1:C1)−(X2:C2)− . . . )=|U Data(cij,ckl, .. . )|/ΣCatNum(cij,clk, . . . )=RecNum(data-specified classificationnumber table, number of classifications>0)/E(data quantity ofdata-specified classification number table)  (28)

In the case of a multidimensional classification axis (society: home,family, health care)−(society: diplomacy, medicine, transplant), forexample, the secondary multidimensional index calculation means 1033calculates the above indexes according to Equations (29) through (32)with reference to the classification-specified data quantity table shownin FIG. 10(A) and the information of FIG. 2 stored in the classifiedhierarchy accumulation unit 201.

Multidimensional Independence((X1:C1)−(X2:C2)− . . . )=1/number ofdimensions×ΣIndependence(Xi:Ci)+1/(2×number ofdimensions)×ΣComDist(Xi,Xj)=½(½×⅙×(ComDist(home, family)+ComDist(home,health care)+ComDist(home, health care))+(½×⅙×(ComDist(diplomacy,medicine)+ComDist(diplomacy, transplant)+ComDist(medicine,transplant))+¼×(ComDist(society,society))=½(½×⅙×(2+2+2)+(½×⅙×(4+4+2))+¼×(0)=0.667  (29)

Multidimensional Specifics((X1:C1)−(X2:C2)− . . . )=1/(Ndimensions)×Σ(depths in classification-specified data quantity table)=1/9×(1+1+1+1+1+1+1+1+1)=1  (30)

Multidimensional Exhaustivity((X1:C1)−(X2:C2)− . . .)=1/DataNum×RecNum(data-specified classification number table, number ofclassifications>0)=⅙×2=0.333  (31)

Multidimensional Uniqueness((X1:C1)−(X2:C2)− . . .)=RecNum(data-specified classification number table, number ofclassifications>0)/Σ(amount of data in data-specified classificationnumber table)=2/(0+1+0+2+0+0)=⅔=0.667  (32)

When the same weight coefficient of 0.25 is set to all indexes, thesecondary multidimensional index calculation means 1033 is able tocalculate priority according to Equation (33).

Priority(X:C)=W1×Independence(X:C)+W2×Specifics(X:C)+W3×Exhaustivity(X:C)+W4×Uniqueness(X:C)=0.25×0.667+0.25×1+0.25×0.333+0.25×0.667=0.67  (33)

As described above, the present embodiment calculates priority based onhierarchical depths of classifications, data quantity of classificationsand data redundancy of classifications, or their combinations inaddition to hierarchical distances of classifications. Thus, it ispossible to effectively reduce the number of times necessary forcalculating priority on multidimensional classification axes in light ofhierarchical depths of classifications, data quantity of classificationsand data redundancy of classifications in addition to hierarchicaldistances of classifications.

Sixth Embodiment

Next, a sixth embodiment of the present invention will be described.FIG. 19 is a block diagram showing an example of the constitution of adata classifier system according to the sixth embodiment. As shown inFIG. 19, the present embodiment differs from the fifth embodiment inthat the data classifier system further includes a multidimensionaldisplay means 1051 in addition to the foregoing constituent elementsshown in FIG. 17.

Specifically, the multidimensional display means 1051 is configured of aCPU of an information processing device which operates according toprograms and a display device such as a display. The multidimensionaldisplay means 1051 implements a function of outputting (displaying)classification axes, priority and data produced by the secondarymultidimensional index calculation means 1033 on a display device. Forinstance, the multidimensional display means 1051 outputs (displays)data quantity, data and attributes correlated to classifications onclassification axes of each dimension in either a list form or a tableform.

First, the operation of the multidimensional display means 1051 fordisplaying information in a list form will be described. FIG. 20 showsan example of information which the multidimensional display means 1051displays in a list form. As shown in FIG. 20, the multidimensionaldisplay means 1051 displays a screen image including areas fordisplaying a multidimensional classification axis, a multidimensionalclassification axis candidate list and a data list. As themultidimensional classification axis, the multidimensional display means1051 displays a multidimensional classification axis having the highestpriority or a classification axis selected from the multidimensionalclassification axis candidate list. In addition, the multidimensionaldisplay means 1051 displays basic categories and classifications in eachdimension, each of which is followed by the amount of data.

FIG. 20 shows an example of a multidimensional classification axis(society: family, health care, transplant)−(society: home, diplomacy,medical care). In this case, the multidimensional display means 1051obtains (extracts) the amount of data correlated to each classificationfrom the data accumulation unit 203 so as to display it.

In an example of FIG. 20, a column representing an item of “other” isnewly added to the screen image displayed on the multidimensionaldisplay means 1051. Herein, the column of “other” below “society” iscorrelated to the basic category of “society”, wherein it is used toclassify data which are not correlated to classifications on eachclassification axis. The column of “other” serving as a child descendantfrom all data is used to describe classifications irrelevant to allclassification axes. In this connection, the multidimensional displaymeans 1051 is able to obtain the amount of data ascribed to the columnof “other” with reference to the information stored in the dataaccumulation unit 203. As the amount of data, the present embodimentembraces the amount of data correlated to descendant classifications.

In an example of FIG. 20, the multidimensional display means 1051displays classification axes whose priorities have been calculated inthe multidimensional classification axis candidate list in an order ofpriorities. The multidimensional display means 1051 is able to obtainmultidimensional classification axes based on the calculation result ofthe secondary multidimensional index calculation means 1033.

In an example of FIG. 20, the multidimensional display means 1051displays various data in the data list. In this case, themultidimensional display means 1051 displays each data correlated withthe data ID, the content and the counterpart classification. Themultidimensional display means 1051 is able to obtain these pieces ofinformation with reference to the information stored in the dataaccumulation unit 203.

In the present embodiment, the functions of the constituent elementsother than the multidimensional display means 1051 are equivalent to thefunctions of the counterpart constituent elements described in the fifthembodiment.

Next, the operation of the multidimensional display means 1051 fordisplaying information in a list form will be described. As an initialrendition, the multidimensional display means 1051 displays amultidimensional classification axis having the highest priority in anarea of displaying the multidimensional classification axis. Inaddition, the multidimensional display means 1051 displays othercandidates of multidimensional classification axes in themultidimensional classification axis candidate list in an order ofpriorities. Furthermore, the multidimensional display means 1051displays all the data accumulated in the data accumulation unit 203 inthe data list.

The multidimensional display means 1051 selects one or pluralclassifications of each dimension on the multidimensional classificationaxis, which is displayed in an area of displaying the multidimensionalclassification axis, thus displaying only the data correlated to all theselected classifications in the data list.

Next, when any one of classification axes on each multidimensionalclassification axis, which is displayed in an area of displaying themultidimensional classification axis candidate list, is selected uponthe user's operation, the multidimensional display means 1051 replacesthe displayed content of the multidimensional classification axis withthe content of the selected multidimensional classification axis.

Although the present embodiment refers to two-dimensional classificationaxes, the multidimensional display means 1051 is able to displaymultidimensional classification axes each ascribed to three or higherdimensions in accordance with the similar process. In this case, themultidimensional display means 1051 displays the data list by addingthree or higher dimensional classification axes to the area ofdisplaying the multidimensional classification axis.

Next, the operation of the multidimensional display means 1051 fordisplaying information in a table form will be described. FIG. 21 showsan example of information which the multidimensional display means 1051displays in a table form. As shown in FIG. 21, the multidimensionaldisplay means 1051 displays a screen image including areas of displayinga multidimensional classification table, a data list and amultidimensional classification axis candidate list.

By use of a multidimensional classification axis having the highestpriority among multidimensional classification axis candidates, themultidimensional display means 1051 displays the multidimensionalclassification table in which the horizontal axis representsfirst-dimensional information whilst the vertical axis representssecond-dimensional information. In the case of the multidimensionalclassification table ascribed to three or higher dimensions, themultidimensional display means 1051 additionally displays s a furtherdimension of information on the vertical axis and/or the horizontalaxis. In the case of a multidimensional classification ascribed to threedimensions, for example, the multidimensional display means 1051displays first-dimensional information on the horizontal axis,adjacently disposes third-dimensional information, and displayssecond-dimensional information on the vertical axis. In addition, themultidimensional display means 1051 displays information representativeof what kind of data exists in each cell of each table. In the presentembodiment, the multidimensional display means 1051 displays data IDsand their numbers.

When any one of cells is selected from the multidimensionalclassification table upon the user's operation, the multidimensionaldisplay means 1051 displays a data list corresponding to the selectedcell. In the present embodiment, the multidimensional display means 1051displays data IDs, contents and classifications. In this connection, themultidimensional display means 1051 displays these pieces of informationwith reference to the information stored in the data accumulation unit203.

The multidimensional display means 1051 displays multidimensionalclassification axes whose priorities have been calculated in themultidimensional classification axis candidate list in an order ofpriorities. The multidimensional display means 1051 is able to obtainthese pieces of information based on the calculation result of thesecondary multidimensional index calculation means 1032.

Next, the operation of the multidimensional display means 1051 fordisplaying information in a table form will be described. In an initialrendition, the multidimensional display means 1051 displays amultidimensional classification axis having the highest priority in themultidimensional classification table. In this case, themultidimensional display means 1051 displays the horizontal axis and thevertical axis in accordance with the foregoing method. In thisconnection, the multidimensional display means 1051 has not yetdisplayed any data in the data list.

Next, when any one of cells is selected from the multidimensionalclassification table upon the user's operation, the multidimensionaldisplay means 1051 displays data corresponding to the selected cell inthe data list.

Next, when any one of multidimensional classification axes is selectedfrom the multidimensional classification axis candidate list upon theuser's operation, the multidimensional display means 1051 displaysselected axes in the multidimensional classification table.

According to the present embodiment described above, multidimensionalclassification axes, priority and data, which are produced by thesecondary multidimensional index calculation means 1033, are displayedin a list form or in a table form. This allows users to visuallyrecognize the selected status of each multidimensional classificationaxis, priority and data.

Seventh Embodiment

Next, a seventh embodiment of the present invention will be described.FIG. 22 is a block diagram showing an example of the constitution of adata classifier system according to the seventh embodiment. As shown inFIG. 22, the present embodiment differs from the sixth embodiment inthat the data classifier system further includes a retrieval means 106in addition to the constituent elements shown in FIG. 19.

Specifically, the retrieval means 106 is configured of a CPU of aninformation processing device which operates according to programs. Theretrieval means 106 implements functions of receiving (inputting)retrieval keywords and classifications and retrieving the storedcontents of the data accumulation unit 203 and other attributeinformation. In addition, the retrieval means 106 implements functionsof obtaining (extracting) retrieval-resultant data IDs and transferring(outputting) them to the classification axis candidate reduction means1021. Upon executing a retrieval process, the retrieval means 106retrieves contents and attribute information by use of an existingfull-text retrieval engine or a relational data base technique.

The present embodiment differs from the sixth embodiment in that thedata classifier system performs processing on a database selectingretrieval-resultant data IDs from the data accumulation unit 203. Otherprocedures executed by the data classifier system are equivalent tothose described in the sixth embodiment.

In the present embodiment, the data classifier system may executeprocessing using the multidimensional index calculation means 1032instead of the secondary multidimensional index calculation means 1033.In addition, the data classifier system does not necessarily include themultidimensional classification axis candidate creation means 1023 whileexecuting processing using the index calculation means 103 or thesecondary index calculation means 1031 instead of the secondarymultidimensional index calculation means 1033. Furthermore, the dataclassifier system may execute processing using the display means 105instead of the multidimensional display means 1051.

According to the above constitution, it is possible to displaymultidimensional classification axes or classification axes based on theuser's retrieval result alone.

As described above, the present embodiment retrieves the stored contentsof the data accumulation unit 203 and other attribute information, thusreducing classification axis candidates with respect to the retrievedinformation. Therefore, upon receiving the classified hierarchy and datagroups correlated to classifications, it is possible to efficientlyreduce times of calculating priority on classification axes.

Eighth Embodiment

Next, an eighth embodiment of the present invention will be described.FIG. 23 is a block diagram showing an example of the constitution of adata classifier system according to the eighth embodiment. As shown inFIG. 23, the present embodiment differs from the seventh embodiment inthat the data classifier system further includes a data correlationmeans 107 in addition to the foregoing constituent elements shown inFIG. 22.

Specifically, the data correlation means 107 is configured of a CPU ofan information processing device which operates according to programs.The data correlation means 107 implements a function of correlating dataand classifications with reference to the information stored in theclassified hierarchy accumulation unit 201 and the information stored inthe data accumulation unit 203. As a correlation method, the datacorrelation means 107 employs existing methods such as a method ofdetecting the occurrence of data representative of classification namesin contents and a method of measuring cosine similarity between datarepresentative of classification names and contents, thus makingcorrelations.

In this connection, it is preferable that the data correlation means 107make correlations before the multidimensional classification axiscandidate creation means 1023 creates classification axis candidates.

The data classifier system of the present embodiment may executeprocessing using the multidimensional index calculation means 1032instead of the secondary multidimensional index calculation means 1033.In addition, the data classifier system does not necessarily include themultidimensional classification axis candidate creation means 1023 sothat it can execute processing using the index calculation means 103 orthe secondary index calculation means 1031 instead of the secondarymultidimensional index calculation means 1033. Furthermore, the dataclassifier system may execute processing using the display means 105instead of the multidimensional display means 1051.

As described above, the present embodiment makes correlations betweendata and classifications with reference to the information stored in theclassified hierarchy accumulation means 201 and the information storedin the data accumulation means 203, thus reducing the number ofclassification axis candidates. Therefore, upon receiving the classifiedhierarchy and data groups correlated to classifications, it is possibleto precisely reduce the number of times necessary for calculatingpriority on classification axes.

Next, the minimum constitution of the data classifier system will bedescribed. FIG. 24 is a block diagram showing the minimum constitutionof the data classifier system. As shown in FIG. 24, the data classifiersystem includes a set of minimum constituent elements, i.e. the basiccategory accumulation unit 202, the classification axis candidatereduction means 1021 and the index calculation means 103.

The basic category accumulation unit 202 accumulates classificationsserving as basic categories which are used to select desiredclassifications. The classification axis candidate reduction means 1021implements functions of selecting a plurality of classifications fromamong classifications descendant from each basic category in advance,creating classification axis candidates and reducing classification axiscandidates subjected to classifications based on data quantity ofclassifications and hierarchical distances of classifications. The indexcalculation means 103 implements a function of calculating priority indisplaying classification axis candidates, the number of which isreduced by the classification axis candidate reduction means 1021.

The data classifier system having the minimum constitution shown in FIG.24 is able to reduce times of calculating priority on classificationaxes upon receiving data groups correlated to the classified hierarchyand classifications.

The aforementioned embodiments illustrate the following constitutionalfeatures (1) through (14) adapted to data classifier systems.

(1) The data classifier system is a system which selects a plurality ofclassifications correlated to data groups so as to output classificationaxes based on hierarchal classifications and data groups. The dataclassifier system includes a basic category accumulation means (e.g. thebasic category accumulation unit 202) which accumulates classificationsserving as basic categories used for selecting desired classificationsin advance, a classification axis candidate reduction means (e.g. theclassification axis candidate reduction means 1021) which selects aplurality of classifications from among classifications descendant fromeach basic category so as to create classification axis candidates andwhich reduces the number of classification axis candidates subjected tocalculations based on data quantity of classifications and hierarchicaldistances of classifications, and a priority calculation means (e.g. theindex calculation means 103) which calculates priority in displayingclassification axis candidates reduced by the classification axiscandidate reduction means.

(2) The data classifier system is configured to select classificationaxes satisfying either a condition in which data quantity ofclassifications has a predetermined value or more or a condition inwhich data quantity of classifications falls within a certain ratiocounting from the highest value, as well as a condition in which lengthsleading to common ancestors among classifications fall within a specificrange.

(3) In the data classifier system, the priority calculation meansdetermines priority based on lengths leading to common ancestors amongclassifications in the classified hierarchy.

(4) In the data classifier system, the priority calculation meanscalculates hierarchical distances of classifications in the classifiedhierarchy so as to determine priority on classification axis candidatesbased on at least one of depths of classifications in the classifiedhierarchy, data quantity of classifications and data redundancy ofclassifications.

(5) The data classifier system includes a display control means (e.g.the display means 105) which inputs classification axis candidatesreduced by the classification axis candidate reduction means andpriority calculated by the priority calculation means so as to performdisplay control on data groups. The display control means displaysclassification axis candidates in an order of priorities, whereindisplayed classification axes are changed in response to selectedclassification axis candidates, and wherein data groups are selected orreduced in response to selected classifications on each classificationaxis.

(6) The data classifier system includes a data retrieval means (e.g. theretrieval means 106) which allows users to retrieve data groups based onretrieval keywords, thus outputting retrieval-resultant data groups tothe classification axis candidate reduction means. The classificationaxis candidate reduction means reduce the number of classification axiscandidates based on the retrieval result of the data retrieval means,whilst the priority calculation means calculates priority onclassification axes correlated to data groups retrieved by the dataretrieval means.

(7) The data classifier system includes a data correlation means (e.g.the data correlation means 107) which inputs hierarchicalclassifications and data groups so as to correlate input classificationsand data groups.

(8) The data classifier system is a system which selects a plurality ofclassifications correlated to data groups so as to create classificationaxes based on hierarchical classifications and their data groups. Thedata classifier system includes a basic category accumulation means(e.g. the basic category accumulation unit 202) which accumulatesclassifications serving as basic categories used for selecting desiredclassifications in advance, a classification axis candidate reductionmeans (e.g. the classification axis candidate reduction means 1021)which creates classification axes based on combinations ofclassifications each corresponding to at least one data amongclassifications descendant from each basic category and which reducesthe number of classification axis candidates subject to calculationsbased on data quantity of classifications and hierarchical distances ofclassifications, a multidimensional classification axis candidatecreation means (e.g. the multidimensional classification axis candidatecreation means 1023) which combine classification axis candidatesreduced by the classification axis candidate reduction means so as tocreate multidimensional classification axis candidates, and amultidimensional priority calculation means (i.e. the multidimensionalindex calculation means 1032) which calculates priority onmultidimensional classification axis candidates based on hierarchicaldistances of classifications in the classified hierarchy.

(9) In the data classifier system, the classification axiscandidate'reduction means selects multidimensional classification axesincluding classifications, in which data quantity correlated toclassifications on classification axes ascribed to each dimension is apredetermined value or more, or data quantity falls within a certainratio counting from the highest value, and classifications in whichhierarchical distances of classifications on classification axesascribed to each dimension, i.e. lengths leading to common ancestorsamong classifications, fall within a specific range.

(10) In the data classifier system, the multidimensional prioritycalculation means changes priority on multidimensional classificationaxes based on lengths leading to common ancestors among classificationson classification axes ascribed to each dimension in the classifiedhierarchy.

(11) In the data classifier system, the multidimensional prioritycalculation means calculates hierarchical distances of classificationson classification axes ascribed to each dimension in the classifiedhierarchy so as to determine priority on multidimensional classificationaxis candidates based on at least one of depths of classifications onclassification axes ascribed to each dimension in the classifiedhierarchy, data quantity of classifications and data redundancy ofclassifications.

(12) The data classifier system includes a multidimensional displaycontrol means (e.g. the multidimensional display means 1051) whichinputs multidimensional classification axis candidates reduced by theclassification axis candidate reduction means and priority calculated bythe multidimensional priority calculation means so as to perform displaycontrol on data groups in a list form or in a table form. Themultidimensional display control means selects multidimensionalclassification axis candidates so as to display classifications ascribedto each dimension in a list form or in a table form, whereby it displaysat least one of data quantity, data names, data attributes andcharacteristic words corresponding to one or plural classificationswhich are selected.

(13) The data classifier system includes a data retrieval means (e.g.the retrieval means 106) which allows user to retrieve data groups basedon retrieval keywords and which outputs retrieval-resultant data groupsto the multidimensional classification axis candidate reduction means.

(14) The data classifier system includes a data correlation means (e.g.the data correlation means 107) which inputs hierarchicalclassifications and data groups so as to correlate input classificationsand data groups.

The present invention is not necessarily limited to the aforementionedembodiments, which can be adequately changed or modified within a rangenot deviating from the scope of the present invention.

INDUSTRIAL APPLICABILITY

The present invention is applicable to document classifier devicesfacilitating outline comprehension of numerous documents and programsrealizing document classifier devices. In addition, the presentinvention is applicable to classified display devices classifying anddisplaying numerous images and programs realizing classified displaydevices.

DESCRIPTION OF THE REFERENCE NUMERALS

-   -   101 Input means    -   103 Index calculation means    -   104 Output means    -   105 Display means    -   106 Retrieval means    -   107 Data correlation means    -   201 Classified hierarchy accumulation unit    -   202 Basic category accumulation unit    -   203 Data accumulation unit    -   1021 Classification axis candidate reduction means    -   1023 Multidimensional classification axis candidate creation        means    -   1033 Secondary index calculation means    -   1032 Multidimensional index calculation means    -   1033 Secondary multidimensional index calculation means    -   1051 Multidimensional display means

1. A data classifier system which selects a plurality of classificationscorrelated to data groups based on hierarchical classifications and datagroups so as to output classification axes, said data classifier systemcomprising: a basic category accumulation means which accumulatesclassifications serving as basic categories used for selecting desiredclassifications in advance; a classification axis candidate reductionmeans which selects a plurality of classifications from amongclassifications descendant from each basic category so as to createclassification axis candidates and which reduces the number ofclassification axis candidates subjected to calculations based on dataquantity of classifications and hierarchical distances ofclassifications; and a priority calculation means which calculatespriority in displaying classification i axis candidates reduced by theclassification axis candidate reduction means.
 2. The data classifiersystem according to claim 1, wherein the classification axis candidatereduction means selects classification axes satisfying either acondition in which data quantity of classifications is a predeterminedvalue or more or a condition in which data quantity of classificationsis ranked in a higher place of ordering than other classifications aswell as a condition in which lengths leading to a common ancestor amongclassifications fall within a specific range.
 3. The data classifiersystem according to claim 1, wherein the priority calculation meansdetermines priority based on lengths leading to a common ancestor amongclassifications in a classified hierarchy.
 4. The data classifier systemaccording to claim 1, wherein the priority calculation means calculateshierarchical distances of classifications in a classified hierarchy anddetermines priority on classification axis candidates based on at leastone of depths of classifications in the classified hierarchy, dataquantity of classifications and data redundancy of classifications. 5.The data classifier system according to claim 1 further comprising adisplay control means which inputs classification axis candidatesreduced by the classification axis candidate reduction means andpriority calculated by the priority calculation means so as to performdisplay control on data groups, wherein the display control meansdisplays classification axis candidates in an order of priorities,changes displayed classification axes in response to classification axiscandidates which are selected, and selects or reduces data groups inresponse to classifications selected for each classification axis. 6.The data classifier system according to claim 1 further comprising adata retrieval means which retrieves data groups based on retrievalkeywords so as to output retrieval-resultant data groups to theclassification axis candidate reduction means, wherein theclassification axis candidate reduction means reduces the number ofclassification axis candidates based on the retrieval result of the dataretrieval means, so that the priority calculation means calculatespriority on classification axes correlated to retrieval-resultant datagroups of the data retrieval means.
 7. The data classifier systemaccording to claim 1 further comprising a data correlation means whichinputs hierarchical classifications and data groups so as to correlateinput classifications and data groups.
 8. A data classifier system whichselects a plurality of classifications correlated to data groups so asto create classification axes based on hierarchical classifications anddata groups and which outputs combinations of classification axes asmultidimensional classification axes, said data classifier systemcomprising: a basic category accumulation means which accumulatesclassifications serving as basic categories used for selecting desiredclassifications in advance; a classification axis candidate reductionmeans which creates classification axes based on combinations ofclassifications, each correlated to at least one data, amongclassifications descendant from each basic category and which reducesthe number of classification axis candidates subjected to calculationsbased on data quantity of classifications and hierarchical distances ofclassifications; a multidimensional classification axis candidatecreation means which creates multidimensional classification axiscandidates combining classification axis candidates reduced by theclassification axis candidate reduction means; and a multidimensionalpriority calculation means which calculates priority on multidimensionalclassification axis candidates based on hierarchical distances ofclassifications in a classified hierarchy.
 9. The data classifier systemaccording to claim 8, wherein the classification axis candidatereduction means selects multidimensional classification axis candidatesincluding classifications satisfying either a condition in which dataquantity of classifications on classification axes ascribed to eachdimension is a predetermined value or more or a condition in which dataquantity of classifications is ranked in a higher place of ordering thanother classifications as well as a condition in which hierarchicaldistances of classifications on classification axes ascribed to eachdimension, i.e. lengths leading to a common ancestor amongclassifications, fall within a specific range.
 10. The data classifiersystem according to claim 8, wherein the multidimensional prioritycalculation means changes priority on multidimensional classificationaxis candidates in response to lengths leading to a common ancestoramong classifications on classification axes ascribed to each dimensionin the classified hierarchy.
 11. The data classifier system according toclaim 8, wherein the multidimensional priority calculation meanscalculates hierarchical distances of classifications on classificationaxes ascribed to each dimension in the classified hierarchy anddetermines priority on multidimensional classification axis candidatesbased on at least one of depths of classifications on classificationaxes ascribed to each dimension in the classified hierarchy, dataquantity of classifications and data redundancy of classifications. 12.The data classifier system according to claim 8 further comprising amultidimensional display control means which inputs multidimensionalclassification axis candidates reduced by the classification axiscandidate reduction means and priority calculated by themultidimensional priority calculation means and which performs displaycontrol on data groups in a list form or in a table form, wherein themultidimensional display control means selects multidimensionalclassification axis candidates so as to dispose and displayclassifications ascribed to each dimension in the list form or in thetable form, thus displaying at least one of data quantity, data names,data attributes and characteristic words corresponding to one or pluralclassifications which are selected.
 13. The data classifier systemaccording to claim 8 further comprising a data retrieval means whichretrieves data groups based on retrieval keywords so as to outputretrieval-resultant data groups to the multidimensional classificationaxis candidate reduction means.
 14. The data classifier system accordingto claim 8 further comprising a data correlation means which inputshierarchical classifications and data groups so as to correlate inputclassifications and data groups.
 15. A data classifier method whichselects a plurality of classifications correlated to data groups so asto output classification axes based on hierarchical classifications anddata groups, said data classifier method comprising: a classificationaxis candidate reduction process which accumulates classificationsserving as basic categories used for selecting desired classificationsin a database in advance, which selects a plurality of classificationsfrom among classifications descendant from each basic category so as tocreate classification axis candidates, and which reduces the number ofclassification axis candidates subjected to calculations based on dataquantity of classifications and hierarchical distances ofclassifications; and a priority calculation process which calculatespriority in displaying the reduced number of classification axiscandidates.
 16. The data classifier method according to claim 15,wherein the classification axis candidate reduction process selectsclassification axes satisfying either a condition in which data quantityof classifications is a predetermined value or more or a condition inwhich data quantity of classifications is ranked in a higher place ofordering than other classifications as well as a condition in whichlengths leading to a common ancestor among classification fall within aspecific range.
 17. The data classifier method according to claim 15,wherein the priority calculation process determines priority based onlengths leading to a common ancestor among classifications in theclassified hierarchy.
 18. The data classifier method according to claim15, wherein the priority calculation process calculates hierarchicaldistances of classifications in the classified hierarchy and determinespriority on classification axis candidates based on at least one ofdepths of classifications in the classified hierarchy, data quantity ofclassifications and data redundancy of classifications.
 19. A dataclassifier method which selects a plurality of classificationscorrelated to data groups based on hierarchical classifications and datagroups so as to create classification axes and which outputsmultidimensional classification axes combining a plurality ofclassification axes, said data classifier method comprising: aclassification axis candidate reduction process which accumulatesclassifications serving as basic categories used for selecting desiredclassifications in a database in advance, which creates classificationaxes based on combinations of classifications correlated to at least onedata among classifications descendant from each basic category, andwhich reduces the number of classification axis candidates subjected tocalculations based on data quantity of classifications and hierarchicaldistances of classifications; a multidimensional classification axiscandidate creation process which creates multidimensional classificationaxis candidates each combining the reduced number of classification axiscandidates; and a multidimensional priority calculation process whichcalculates priority on multidimensional classification axis candidatesbased on hierarchical distances of classifications in a classifiedhierarchy.
 20. The data classifier method according to claim 19, whereinthe classification axis candidate reduction process selectsmultidimensional classification axis candidates includingclassifications, in which data quantity of classifications onclassification axes ascribed to each dimension is predetermined value ormore or in which data quantity of classification falls within a certainratio counting from a highest value, and classifications in whichhierarchical distances of classifications on classification axesascribed to each dimension, i.e. lengths leading to a common ancestoramong classifications, fall within a specific range.
 21. The dataclassifier method according to claim 19, wherein the multidimensionalpriority calculation process changes priority on multidimensionalclassification axes in response to lengths leading to a common ancestoramong classifications on classification axes ascribed to each dimensionin the classified hierarchy.
 22. The data classifier method according toclaim 19, wherein the multidimensional priority calculation processcalculates hierarchical distances of classifications on classificationaxes ascribed to each dimension in the classified hierarchy anddetermines priority on multidimensional classification axis candidatesbased on at least one of depths of classifications on classificationaxes ascribed to each dimension in the classified hierarchy, dataquantity of classifications and data redundancy of classifications. 23.A data classifier program which selects a plurality of classificationscorrelated to data groups so as to output classification axes based onhierarchical distances and data groups, said data classifier programcausing a computer having a basic category accumulation means, whichaccumulates classifications serving as basic categoris used forselecting desired classifications, to execute a classification axiscandidate reduction process which selects a plurality of classificationsfrom among classifications descendant from each basic category so as tocreate classification axis candidates and which reduces the number ofclassification axis candidates subjected to calculations based on dataquantity of classifications and hierarchical distances ofclassifications, and a priority calculation process which calculatespriority in displaying the reduced number of classification axiscandidates.
 24. The data classifier program according to claim 23, saiddata classifier program causing a computer to execute the classificationaxis candidate reduction process in such a way that classification axesare selected to satisfy either a condition in which data quantity ofclassifications is a predetermined value or more or a condition in whichdata quantity of classifications is ranked in a higher place of orderingthan other classifications as well as a condition in which lengthsleading to a common ancestor among classifications fall within aspecific range.
 25. The data classifier program according to claim 23,said data classifier program causing a computer to execute a displaycontrol process which inputs classification axis candidates reduced bythe classification axis candidate reduction process and prioritycalculates by the priority calculation process so as to perform displaycontrol on data groups, wherein the display control process displaysclassification axis candidates in an order of priorities, changesdisplayed classification axes in response to selected classificationaxis candidates, and selects or reduces data groups in response toselected classifications on classification axes.
 26. The data classifierprogram according to claim 23, said data classifier program causing acomputer to execute a data retrieval process which retrieves data groupsbased on retrieval keywords so as to output retrieval-resultant datagroups, wherein the classification axis candidate reduction processreduces the number of classification axis candidates based on theretrieval result of the data retrieval process, so that the prioritycalculation process calculates priority on classification axescorrelated to data groups retrieved by the data retrieval process. 27.The data classifier program according to claim 23, said data classifierprogram causing a computer to execute a data correlation process whichinputs hierarchical classifications and data groups so as to correlateinput classifications and data groups.
 28. A data classifier programwhich selects a plurality of classifications correlated to data groupsbased on hierarchical classifications and data groups so as to createclassification axes and which outputs multidimensional classificationaxes combining a plurality of classification axes, said data classifierprogram causing a computer having a basic category accumulation means,which accumulates classifications serving as basic categories used forselecting desired classifications in advance, to execute aclassification axis candidate reduction process which createsclassification axes based on combinations of classifications correlatedto at least one data among classifications descendant from each basiccategory, and which reduces the number of classification axis candidatessubjected to calculations based on data quantity of classifications andhierarchical distances of classifications, a multidimensionalclassification axis candidate creation process which createsmultidimensional classification axis candidates each combining thereduced number of classification axis candidates, and a multidimensionalpriority calculation process which calculates priority onmultidimensional classification axis candidates based on hierarchicaldistances of classifications in a classified hierarchy.
 29. The dataclassifier program according to claim 28, said data classifier programcausing a computer to execute the classification axis candidatereduction process to select multidimensional classification axesincluding classifications in which data quantity of classifications is apredetermined value or more or in which data quantity of classificationsfalls within a certain ratio counting from a highest value, as well asclassifications whose lengths leading to a common ancestor fall within aspecific range.
 30. The data classifier program according to claim 28,said data classifier program causing a computer to execute amultidimensional display control process which inputs multidimensionalclassification axis candidates reduced by the classification axiscandidate reduction process and priority calculated by themultidimensional priority calculation process and which performs displaycontrol on data groups in a list form or in a table form, wherein themultidimensional display control process selects multidimensionalclassification axis candidates so as to dispose and displayclassifications ascribed to each dimension in a list form or in a tableform, thus displaying at least one of data quantity, data names, dataattributes and characteristic words in response to one or pluralclassifications which are selected.
 31. The data classifier programaccording to claim 28, said data classifier program causing a computerto execute a data retrieval process which retrieves data groups based onretrieval keywords so as to output retrieval-resultant data groups. 32.The data classifier program according to claim 28, said data classifierprogram causing a computer to execute a data correlation process whichinputs hierarchical classifications and data groups so as to correlateinput classifications and data groups.